expository essay on scientific method

Induction, Deduction, and the Scientific Method Expository Essay

Introduction, works cited.

Nature is a huge non-empty space that harbours both real and imaginary components. Theologists and Scientists differ much on the existence of these components. While theology accepts both, Science focuses on the real nature owing to the fact that it can test its existence. Of interest for Scientists is the methods employed to deal with a problem, which range from inductive to deductive.

Nature can tempt one to conclude that he/she is conversant with a thing while the opposite is the case. Scientific methods provide a solution for this. Though contradicting, the two methods are practically differently. For instance, for induction, a system is examined first followed by an inference based on the observations. Deduction takes the reverse. Although they share a lot and yield a solution, inductive method is quite open-ended as compared to deductive.

A problem forms the basis of any scientific method, inductive or deductive. It triggers scientists into developing criteria that fights the problem whether simple or complicated. “Solution of problems…is achieved by long strings of mixed inductive and deductive inferences…” (Pirsig Para. 9). It is importance to state the problem right otherwise whichever method used, it will never yield a valid solution.

Hypothesis follows the question immediately. It provides a tentative answer to the aforementioned problem before further research. It may be one, two, or three as desired by the researcher. If more than two, the experiment should be set to test their validity in relation to the problem. This reduces them to at least two usually referred to as the null (H 0 ) and the alternative (H 1 ). According to Fischer, it is the hypothesis that illustrates what is expected in a research (43).

Experimentation is a very crucial level. This is why scientists agree only on what experiments have proved. This step distinguishes Science from other disciplines.

Box says that the majority’s view of science as all about experiments is a mere thought (134). It is from the experiment that data arises that is used to prove wrong or right the null hypothesis. The experiment can fail or succeed. Robert says that failure occurs only when the experiment does not prove the cited hypothesis but not the predicted outcomes.

Before computing the results from the already collected data, predictions are made. This part of induction fosters confidence level of the researcher. It shows how the validity of the hypothesis will be illustrated. Lindley says that once developed, this prediction need not be changed even if it contradicts the experimental findings (56). This is in accordance with Robert’s illustrations in the handout. A wrong guess is not an indicator of a “discontent” but a source of new ideas brought by the experiment.

As part of scientific methods, observation of the experimental findings follows. It occurs both in inductive and deductive methods. These observations are compared with the predicted outcomes. It calls for a lot of practice and scientific computation before one declares the null hypothesis true or false. According to Schervish, the observations will be significant if they did not occur by mistake but through the experiment (218).

The idea behind science is to clear doubts about the existing theories. The destination of any scientific method is the verification level or simply the conclusion. This proves whether the problem on study is solved or not. Possible improvements can be suggested after this confirmation level. Regardless of size of the problem solved, this stage curbs what scientists reject most: assumptions.

The inductive and deductive approaches to problem solving play a major role in the scientific world. The ever-increasing theories have driven scientists into the task of proving or disapproving them. This explains why they prefer the deductive to the inductive approach. It begins with the theory and ends with its confirmation.

Box, Joan. “The Life of a Scientist.” New York: Wiley. 1978, p. 134.

Fisher, Richard. “Statistical Methods for Research Workers.” Edinburgh: Oliver and Boyd, 1925, p.43.

Lindley, David. “Making Decisions.” (2nd Ed.). John Wiley & Sons. 1985.

Pirsig, Robert. “Induction, Deduction and the Scientific Method.” N.d. Web.

Schervish, Michael. “Theory of Statistics.” Springer: 1995. p.218.

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How to Write an Expository Essay | Structure, Tips & Examples

Published on July 14, 2020 by Jack Caulfield . Revised on July 23, 2023.

“Expository” means “intended to explain or describe something.” An expository essay provides a clear, focused explanation of a particular topic, process, or set of ideas. It doesn’t set out to prove a point, just to give a balanced view of its subject matter.

Expository essays are usually short assignments intended to test your composition skills or your understanding of a subject. They tend to involve less research and original arguments than argumentative essays .

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When should you write an expository essay, how to approach an expository essay, introducing your essay, writing the body paragraphs, concluding your essay, other interesting articles, frequently asked questions about expository essays.

In school and university, you might have to write expository essays as in-class exercises, exam questions, or coursework assignments.

Sometimes it won’t be directly stated that the assignment is an expository essay, but there are certain keywords that imply expository writing is required. Consider the prompts below.

The word “explain” here is the clue: An essay responding to this prompt should provide an explanation of this historical process—not necessarily an original argument about it.

Sometimes you’ll be asked to define a particular term or concept. This means more than just copying down the dictionary definition; you’ll be expected to explore different ideas surrounding the term, as this prompt emphasizes.

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An expository essay should take an objective approach: It isn’t about your personal opinions or experiences. Instead, your goal is to provide an informative and balanced explanation of your topic. Avoid using the first or second person (“I” or “you”).

The structure of your expository essay will vary according to the scope of your assignment and the demands of your topic. It’s worthwhile to plan out your structure before you start, using an essay outline .

A common structure for a short expository essay consists of five paragraphs: An introduction, three body paragraphs, and a conclusion.

Like all essays, an expository essay begins with an introduction . This serves to hook the reader’s interest, briefly introduce your topic, and provide a thesis statement summarizing what you’re going to say about it.

Hover over different parts of the example below to see how a typical introduction works.

In many ways, the invention of the printing press marked the end of the Middle Ages. The medieval period in Europe is often remembered as a time of intellectual and political stagnation. Prior to the Renaissance, the average person had very limited access to books and was unlikely to be literate. The invention of the printing press in the 15th century allowed for much less restricted circulation of information in Europe, paving the way for the Reformation.

The body of your essay is where you cover your topic in depth. It often consists of three paragraphs, but may be more for a longer essay. This is where you present the details of the process, idea or topic you’re explaining.

It’s important to make sure each paragraph covers its own clearly defined topic, introduced with a topic sentence . Different topics (all related to the overall subject matter of the essay) should be presented in a logical order, with clear transitions between paragraphs.

Hover over different parts of the example paragraph below to see how a body paragraph is constructed.

The invention of the printing press in 1440 changed this situation dramatically. Johannes Gutenberg, who had worked as a goldsmith, used his knowledge of metals in the design of the press. He made his type from an alloy of lead, tin, and antimony, whose durability allowed for the reliable production of high-quality books. This new technology allowed texts to be reproduced and disseminated on a much larger scale than was previously possible. The Gutenberg Bible appeared in the 1450s, and a large number of printing presses sprang up across the continent in the following decades. Gutenberg’s invention rapidly transformed cultural production in Europe; among other things, it would lead to the Protestant Reformation.

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The conclusion of an expository essay serves to summarize the topic under discussion. It should not present any new information or evidence, but should instead focus on reinforcing the points made so far. Essentially, your conclusion is there to round off the essay in an engaging way.

Hover over different parts of the example below to see how a conclusion works.

The invention of the printing press was important not only in terms of its immediate cultural and economic effects, but also in terms of its major impact on politics and religion across Europe. In the century following the invention of the printing press, the relatively stationary intellectual atmosphere of the Middle Ages gave way to the social upheavals of the Reformation and the Renaissance. A single technological innovation had contributed to the total reshaping of the continent.

If you want to know more about AI tools , college essays , or fallacies make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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An expository essay is a broad form that varies in length according to the scope of the assignment.

Expository essays are often assigned as a writing exercise or as part of an exam, in which case a five-paragraph essay of around 800 words may be appropriate.

You’ll usually be given guidelines regarding length; if you’re not sure, ask.

An expository essay is a common assignment in high-school and university composition classes. It might be assigned as coursework, in class, or as part of an exam.

Sometimes you might not be told explicitly to write an expository essay. Look out for prompts containing keywords like “explain” and “define.” An expository essay is usually the right response to these prompts.

An argumentative essay tends to be a longer essay involving independent research, and aims to make an original argument about a topic. Its thesis statement makes a contentious claim that must be supported in an objective, evidence-based way.

An expository essay also aims to be objective, but it doesn’t have to make an original argument. Rather, it aims to explain something (e.g., a process or idea) in a clear, concise way. Expository essays are often shorter assignments and rely less on research.

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Scientific Method

Science is an enormously successful human enterprise. The study of scientific method is the attempt to discern the activities by which that success is achieved. Among the activities often identified as characteristic of science are systematic observation and experimentation, inductive and deductive reasoning, and the formation and testing of hypotheses and theories. How these are carried out in detail can vary greatly, but characteristics like these have been looked to as a way of demarcating scientific activity from non-science, where only enterprises which employ some canonical form of scientific method or methods should be considered science (see also the entry on science and pseudo-science ). Others have questioned whether there is anything like a fixed toolkit of methods which is common across science and only science. Some reject privileging one view of method as part of rejecting broader views about the nature of science, such as naturalism (Dupré 2004); some reject any restriction in principle (pluralism).

Scientific method should be distinguished from the aims and products of science, such as knowledge, predictions, or control. Methods are the means by which those goals are achieved. Scientific method should also be distinguished from meta-methodology, which includes the values and justifications behind a particular characterization of scientific method (i.e., a methodology) — values such as objectivity, reproducibility, simplicity, or past successes. Methodological rules are proposed to govern method and it is a meta-methodological question whether methods obeying those rules satisfy given values. Finally, method is distinct, to some degree, from the detailed and contextual practices through which methods are implemented. The latter might range over: specific laboratory techniques; mathematical formalisms or other specialized languages used in descriptions and reasoning; technological or other material means; ways of communicating and sharing results, whether with other scientists or with the public at large; or the conventions, habits, enforced customs, and institutional controls over how and what science is carried out.

While it is important to recognize these distinctions, their boundaries are fuzzy. Hence, accounts of method cannot be entirely divorced from their methodological and meta-methodological motivations or justifications, Moreover, each aspect plays a crucial role in identifying methods. Disputes about method have therefore played out at the detail, rule, and meta-rule levels. Changes in beliefs about the certainty or fallibility of scientific knowledge, for instance (which is a meta-methodological consideration of what we can hope for methods to deliver), have meant different emphases on deductive and inductive reasoning, or on the relative importance attached to reasoning over observation (i.e., differences over particular methods.) Beliefs about the role of science in society will affect the place one gives to values in scientific method.

The issue which has shaped debates over scientific method the most in the last half century is the question of how pluralist do we need to be about method? Unificationists continue to hold out for one method essential to science; nihilism is a form of radical pluralism, which considers the effectiveness of any methodological prescription to be so context sensitive as to render it not explanatory on its own. Some middle degree of pluralism regarding the methods embodied in scientific practice seems appropriate. But the details of scientific practice vary with time and place, from institution to institution, across scientists and their subjects of investigation. How significant are the variations for understanding science and its success? How much can method be abstracted from practice? This entry describes some of the attempts to characterize scientific method or methods, as well as arguments for a more context-sensitive approach to methods embedded in actual scientific practices.

1. Overview and organizing themes

2. historical review: aristotle to mill, 3.1 logical constructionism and operationalism, 3.2. h-d as a logic of confirmation, 3.3. popper and falsificationism, 3.4 meta-methodology and the end of method, 4. statistical methods for hypothesis testing, 5.1 creative and exploratory practices.

• 5.2 Computer methods and the ‘new ways’ of doing science

6.1 “The scientific method” in science education and as seen by scientists

6.2 privileged methods and ‘gold standards’, 6.3 scientific method in the court room, 6.4 deviating practices, 7. conclusion, other internet resources, related entries.

This entry could have been given the title Scientific Methods and gone on to fill volumes, or it could have been extremely short, consisting of a brief summary rejection of the idea that there is any such thing as a unique Scientific Method at all. Both unhappy prospects are due to the fact that scientific activity varies so much across disciplines, times, places, and scientists that any account which manages to unify it all will either consist of overwhelming descriptive detail, or trivial generalizations.

The choice of scope for the present entry is more optimistic, taking a cue from the recent movement in philosophy of science toward a greater attention to practice: to what scientists actually do. This “turn to practice” can be seen as the latest form of studies of methods in science, insofar as it represents an attempt at understanding scientific activity, but through accounts that are neither meant to be universal and unified, nor singular and narrowly descriptive. To some extent, different scientists at different times and places can be said to be using the same method even though, in practice, the details are different.

Whether the context in which methods are carried out is relevant, or to what extent, will depend largely on what one takes the aims of science to be and what one’s own aims are. For most of the history of scientific methodology the assumption has been that the most important output of science is knowledge and so the aim of methodology should be to discover those methods by which scientific knowledge is generated.

Science was seen to embody the most successful form of reasoning (but which form?) to the most certain knowledge claims (but how certain?) on the basis of systematically collected evidence (but what counts as evidence, and should the evidence of the senses take precedence, or rational insight?) Section 2 surveys some of the history, pointing to two major themes. One theme is seeking the right balance between observation and reasoning (and the attendant forms of reasoning which employ them); the other is how certain scientific knowledge is or can be.

Section 3 turns to 20 th century debates on scientific method. In the second half of the 20 th century the epistemic privilege of science faced several challenges and many philosophers of science abandoned the reconstruction of the logic of scientific method. Views changed significantly regarding which functions of science ought to be captured and why. For some, the success of science was better identified with social or cultural features. Historical and sociological turns in the philosophy of science were made, with a demand that greater attention be paid to the non-epistemic aspects of science, such as sociological, institutional, material, and political factors. Even outside of those movements there was an increased specialization in the philosophy of science, with more and more focus on specific fields within science. The combined upshot was very few philosophers arguing any longer for a grand unified methodology of science. Sections 3 and 4 surveys the main positions on scientific method in 20 th century philosophy of science, focusing on where they differ in their preference for confirmation or falsification or for waiving the idea of a special scientific method altogether.

In recent decades, attention has primarily been paid to scientific activities traditionally falling under the rubric of method, such as experimental design and general laboratory practice, the use of statistics, the construction and use of models and diagrams, interdisciplinary collaboration, and science communication. Sections 4–6 attempt to construct a map of the current domains of the study of methods in science.

As these sections illustrate, the question of method is still central to the discourse about science. Scientific method remains a topic for education, for science policy, and for scientists. It arises in the public domain where the demarcation or status of science is at issue. Some philosophers have recently returned, therefore, to the question of what it is that makes science a unique cultural product. This entry will close with some of these recent attempts at discerning and encapsulating the activities by which scientific knowledge is achieved.

Attempting a history of scientific method compounds the vast scope of the topic. This section briefly surveys the background to modern methodological debates. What can be called the classical view goes back to antiquity, and represents a point of departure for later divergences. [ 1 ]

We begin with a point made by Laudan (1968) in his historical survey of scientific method:

Perhaps the most serious inhibition to the emergence of the history of theories of scientific method as a respectable area of study has been the tendency to conflate it with the general history of epistemology, thereby assuming that the narrative categories and classificatory pigeon-holes applied to the latter are also basic to the former. (1968: 5)

To see knowledge about the natural world as falling under knowledge more generally is an understandable conflation. Histories of theories of method would naturally employ the same narrative categories and classificatory pigeon holes. An important theme of the history of epistemology, for example, is the unification of knowledge, a theme reflected in the question of the unification of method in science. Those who have identified differences in kinds of knowledge have often likewise identified different methods for achieving that kind of knowledge (see the entry on the unity of science ).

Different views on what is known, how it is known, and what can be known are connected. Plato distinguished the realms of things into the visible and the intelligible ( The Republic , 510a, in Cooper 1997). Only the latter, the Forms, could be objects of knowledge. The intelligible truths could be known with the certainty of geometry and deductive reasoning. What could be observed of the material world, however, was by definition imperfect and deceptive, not ideal. The Platonic way of knowledge therefore emphasized reasoning as a method, downplaying the importance of observation. Aristotle disagreed, locating the Forms in the natural world as the fundamental principles to be discovered through the inquiry into nature ( Metaphysics Z , in Barnes 1984).

Aristotle is recognized as giving the earliest systematic treatise on the nature of scientific inquiry in the western tradition, one which embraced observation and reasoning about the natural world. In the Prior and Posterior Analytics , Aristotle reflects first on the aims and then the methods of inquiry into nature. A number of features can be found which are still considered by most to be essential to science. For Aristotle, empiricism, careful observation (but passive observation, not controlled experiment), is the starting point. The aim is not merely recording of facts, though. For Aristotle, science ( epistêmê ) is a body of properly arranged knowledge or learning—the empirical facts, but also their ordering and display are of crucial importance. The aims of discovery, ordering, and display of facts partly determine the methods required of successful scientific inquiry. Also determinant is the nature of the knowledge being sought, and the explanatory causes proper to that kind of knowledge (see the discussion of the four causes in the entry on Aristotle on causality ).

In addition to careful observation, then, scientific method requires a logic as a system of reasoning for properly arranging, but also inferring beyond, what is known by observation. Methods of reasoning may include induction, prediction, or analogy, among others. Aristotle’s system (along with his catalogue of fallacious reasoning) was collected under the title the Organon . This title would be echoed in later works on scientific reasoning, such as Novum Organon by Francis Bacon, and Novum Organon Restorum by William Whewell (see below). In Aristotle’s Organon reasoning is divided primarily into two forms, a rough division which persists into modern times. The division, known most commonly today as deductive versus inductive method, appears in other eras and methodologies as analysis/​synthesis, non-ampliative/​ampliative, or even confirmation/​verification. The basic idea is there are two “directions” to proceed in our methods of inquiry: one away from what is observed, to the more fundamental, general, and encompassing principles; the other, from the fundamental and general to instances or implications of principles.

The basic aim and method of inquiry identified here can be seen as a theme running throughout the next two millennia of reflection on the correct way to seek after knowledge: carefully observe nature and then seek rules or principles which explain or predict its operation. The Aristotelian corpus provided the framework for a commentary tradition on scientific method independent of science itself (cosmos versus physics.) During the medieval period, figures such as Albertus Magnus (1206–1280), Thomas Aquinas (1225–1274), Robert Grosseteste (1175–1253), Roger Bacon (1214/1220–1292), William of Ockham (1287–1347), Andreas Vesalius (1514–1546), Giacomo Zabarella (1533–1589) all worked to clarify the kind of knowledge obtainable by observation and induction, the source of justification of induction, and best rules for its application. [ 2 ] Many of their contributions we now think of as essential to science (see also Laudan 1968). As Aristotle and Plato had employed a framework of reasoning either “to the forms” or “away from the forms”, medieval thinkers employed directions away from the phenomena or back to the phenomena. In analysis, a phenomena was examined to discover its basic explanatory principles; in synthesis, explanations of a phenomena were constructed from first principles.

During the Scientific Revolution these various strands of argument, experiment, and reason were forged into a dominant epistemic authority. The 16 th –18 th centuries were a period of not only dramatic advance in knowledge about the operation of the natural world—advances in mechanical, medical, biological, political, economic explanations—but also of self-awareness of the revolutionary changes taking place, and intense reflection on the source and legitimation of the method by which the advances were made. The struggle to establish the new authority included methodological moves. The Book of Nature, according to the metaphor of Galileo Galilei (1564–1642) or Francis Bacon (1561–1626), was written in the language of mathematics, of geometry and number. This motivated an emphasis on mathematical description and mechanical explanation as important aspects of scientific method. Through figures such as Henry More and Ralph Cudworth, a neo-Platonic emphasis on the importance of metaphysical reflection on nature behind appearances, particularly regarding the spiritual as a complement to the purely mechanical, remained an important methodological thread of the Scientific Revolution (see the entries on Cambridge platonists ; Boyle ; Henry More ; Galileo ).

In Novum Organum (1620), Bacon was critical of the Aristotelian method for leaping from particulars to universals too quickly. The syllogistic form of reasoning readily mixed those two types of propositions. Bacon aimed at the invention of new arts, principles, and directions. His method would be grounded in methodical collection of observations, coupled with correction of our senses (and particularly, directions for the avoidance of the Idols, as he called them, kinds of systematic errors to which naïve observers are prone.) The community of scientists could then climb, by a careful, gradual and unbroken ascent, to reliable general claims.

Bacon’s method has been criticized as impractical and too inflexible for the practicing scientist. Whewell would later criticize Bacon in his System of Logic for paying too little attention to the practices of scientists. It is hard to find convincing examples of Bacon’s method being put in to practice in the history of science, but there are a few who have been held up as real examples of 16 th century scientific, inductive method, even if not in the rigid Baconian mold: figures such as Robert Boyle (1627–1691) and William Harvey (1578–1657) (see the entry on Bacon ).

It is to Isaac Newton (1642–1727), however, that historians of science and methodologists have paid greatest attention. Given the enormous success of his Principia Mathematica and Opticks , this is understandable. The study of Newton’s method has had two main thrusts: the implicit method of the experiments and reasoning presented in the Opticks, and the explicit methodological rules given as the Rules for Philosophising (the Regulae) in Book III of the Principia . [ 3 ] Newton’s law of gravitation, the linchpin of his new cosmology, broke with explanatory conventions of natural philosophy, first for apparently proposing action at a distance, but more generally for not providing “true”, physical causes. The argument for his System of the World ( Principia , Book III) was based on phenomena, not reasoned first principles. This was viewed (mainly on the continent) as insufficient for proper natural philosophy. The Regulae counter this objection, re-defining the aims of natural philosophy by re-defining the method natural philosophers should follow. (See the entry on Newton’s philosophy .)

To his list of methodological prescriptions should be added Newton’s famous phrase “ hypotheses non fingo ” (commonly translated as “I frame no hypotheses”.) The scientist was not to invent systems but infer explanations from observations, as Bacon had advocated. This would come to be known as inductivism. In the century after Newton, significant clarifications of the Newtonian method were made. Colin Maclaurin (1698–1746), for instance, reconstructed the essential structure of the method as having complementary analysis and synthesis phases, one proceeding away from the phenomena in generalization, the other from the general propositions to derive explanations of new phenomena. Denis Diderot (1713–1784) and editors of the Encyclopédie did much to consolidate and popularize Newtonianism, as did Francesco Algarotti (1721–1764). The emphasis was often the same, as much on the character of the scientist as on their process, a character which is still commonly assumed. The scientist is humble in the face of nature, not beholden to dogma, obeys only his eyes, and follows the truth wherever it leads. It was certainly Voltaire (1694–1778) and du Chatelet (1706–1749) who were most influential in propagating the latter vision of the scientist and their craft, with Newton as hero. Scientific method became a revolutionary force of the Enlightenment. (See also the entries on Newton , Leibniz , Descartes , Boyle , Hume , enlightenment , as well as Shank 2008 for a historical overview.)

Not all 18 th century reflections on scientific method were so celebratory. Famous also are George Berkeley’s (1685–1753) attack on the mathematics of the new science, as well as the over-emphasis of Newtonians on observation; and David Hume’s (1711–1776) undermining of the warrant offered for scientific claims by inductive justification (see the entries on: George Berkeley ; David Hume ; Hume’s Newtonianism and Anti-Newtonianism ). Hume’s problem of induction motivated Immanuel Kant (1724–1804) to seek new foundations for empirical method, though as an epistemic reconstruction, not as any set of practical guidelines for scientists. Both Hume and Kant influenced the methodological reflections of the next century, such as the debate between Mill and Whewell over the certainty of inductive inferences in science.

The debate between John Stuart Mill (1806–1873) and William Whewell (1794–1866) has become the canonical methodological debate of the 19 th century. Although often characterized as a debate between inductivism and hypothetico-deductivism, the role of the two methods on each side is actually more complex. On the hypothetico-deductive account, scientists work to come up with hypotheses from which true observational consequences can be deduced—hence, hypothetico-deductive. Because Whewell emphasizes both hypotheses and deduction in his account of method, he can be seen as a convenient foil to the inductivism of Mill. However, equally if not more important to Whewell’s portrayal of scientific method is what he calls the “fundamental antithesis”. Knowledge is a product of the objective (what we see in the world around us) and subjective (the contributions of our mind to how we perceive and understand what we experience, which he called the Fundamental Ideas). Both elements are essential according to Whewell, and he was therefore critical of Kant for too much focus on the subjective, and John Locke (1632–1704) and Mill for too much focus on the senses. Whewell’s fundamental ideas can be discipline relative. An idea can be fundamental even if it is necessary for knowledge only within a given scientific discipline (e.g., chemical affinity for chemistry). This distinguishes fundamental ideas from the forms and categories of intuition of Kant. (See the entry on Whewell .)

Clarifying fundamental ideas would therefore be an essential part of scientific method and scientific progress. Whewell called this process “Discoverer’s Induction”. It was induction, following Bacon or Newton, but Whewell sought to revive Bacon’s account by emphasising the role of ideas in the clear and careful formulation of inductive hypotheses. Whewell’s induction is not merely the collecting of objective facts. The subjective plays a role through what Whewell calls the Colligation of Facts, a creative act of the scientist, the invention of a theory. A theory is then confirmed by testing, where more facts are brought under the theory, called the Consilience of Inductions. Whewell felt that this was the method by which the true laws of nature could be discovered: clarification of fundamental concepts, clever invention of explanations, and careful testing. Mill, in his critique of Whewell, and others who have cast Whewell as a fore-runner of the hypothetico-deductivist view, seem to have under-estimated the importance of this discovery phase in Whewell’s understanding of method (Snyder 1997a,b, 1999). Down-playing the discovery phase would come to characterize methodology of the early 20 th century (see section 3 ).

Mill, in his System of Logic , put forward a narrower view of induction as the essence of scientific method. For Mill, induction is the search first for regularities among events. Among those regularities, some will continue to hold for further observations, eventually gaining the status of laws. One can also look for regularities among the laws discovered in a domain, i.e., for a law of laws. Which “law law” will hold is time and discipline dependent and open to revision. One example is the Law of Universal Causation, and Mill put forward specific methods for identifying causes—now commonly known as Mill’s methods. These five methods look for circumstances which are common among the phenomena of interest, those which are absent when the phenomena are, or those for which both vary together. Mill’s methods are still seen as capturing basic intuitions about experimental methods for finding the relevant explanatory factors ( System of Logic (1843), see Mill entry). The methods advocated by Whewell and Mill, in the end, look similar. Both involve inductive generalization to covering laws. They differ dramatically, however, with respect to the necessity of the knowledge arrived at; that is, at the meta-methodological level (see the entries on Whewell and Mill entries).

3. Logic of method and critical responses

The quantum and relativistic revolutions in physics in the early 20 th century had a profound effect on methodology. Conceptual foundations of both theories were taken to show the defeasibility of even the most seemingly secure intuitions about space, time and bodies. Certainty of knowledge about the natural world was therefore recognized as unattainable. Instead a renewed empiricism was sought which rendered science fallible but still rationally justifiable.

Analyses of the reasoning of scientists emerged, according to which the aspects of scientific method which were of primary importance were the means of testing and confirming of theories. A distinction in methodology was made between the contexts of discovery and justification. The distinction could be used as a wedge between the particularities of where and how theories or hypotheses are arrived at, on the one hand, and the underlying reasoning scientists use (whether or not they are aware of it) when assessing theories and judging their adequacy on the basis of the available evidence. By and large, for most of the 20 th century, philosophy of science focused on the second context, although philosophers differed on whether to focus on confirmation or refutation as well as on the many details of how confirmation or refutation could or could not be brought about. By the mid-20 th century these attempts at defining the method of justification and the context distinction itself came under pressure. During the same period, philosophy of science developed rapidly, and from section 4 this entry will therefore shift from a primarily historical treatment of the scientific method towards a primarily thematic one.

Advances in logic and probability held out promise of the possibility of elaborate reconstructions of scientific theories and empirical method, the best example being Rudolf Carnap’s The Logical Structure of the World (1928). Carnap attempted to show that a scientific theory could be reconstructed as a formal axiomatic system—that is, a logic. That system could refer to the world because some of its basic sentences could be interpreted as observations or operations which one could perform to test them. The rest of the theoretical system, including sentences using theoretical or unobservable terms (like electron or force) would then either be meaningful because they could be reduced to observations, or they had purely logical meanings (called analytic, like mathematical identities). This has been referred to as the verifiability criterion of meaning. According to the criterion, any statement not either analytic or verifiable was strictly meaningless. Although the view was endorsed by Carnap in 1928, he would later come to see it as too restrictive (Carnap 1956). Another familiar version of this idea is operationalism of Percy William Bridgman. In The Logic of Modern Physics (1927) Bridgman asserted that every physical concept could be defined in terms of the operations one would perform to verify the application of that concept. Making good on the operationalisation of a concept even as simple as length, however, can easily become enormously complex (for measuring very small lengths, for instance) or impractical (measuring large distances like light years.)

Carl Hempel’s (1950, 1951) criticisms of the verifiability criterion of meaning had enormous influence. He pointed out that universal generalizations, such as most scientific laws, were not strictly meaningful on the criterion. Verifiability and operationalism both seemed too restrictive to capture standard scientific aims and practice. The tenuous connection between these reconstructions and actual scientific practice was criticized in another way. In both approaches, scientific methods are instead recast in methodological roles. Measurements, for example, were looked to as ways of giving meanings to terms. The aim of the philosopher of science was not to understand the methods per se , but to use them to reconstruct theories, their meanings, and their relation to the world. When scientists perform these operations, however, they will not report that they are doing them to give meaning to terms in a formal axiomatic system. This disconnect between methodology and the details of actual scientific practice would seem to violate the empiricism the Logical Positivists and Bridgman were committed to. The view that methodology should correspond to practice (to some extent) has been called historicism, or intuitionism. We turn to these criticisms and responses in section 3.4 . [ 4 ]

Positivism also had to contend with the recognition that a purely inductivist approach, along the lines of Bacon-Newton-Mill, was untenable. There was no pure observation, for starters. All observation was theory laden. Theory is required to make any observation, therefore not all theory can be derived from observation alone. (See the entry on theory and observation in science .) Even granting an observational basis, Hume had already pointed out that one could not deductively justify inductive conclusions without begging the question by presuming the success of the inductive method. Likewise, positivist attempts at analyzing how a generalization can be confirmed by observations of its instances were subject to a number of criticisms. Goodman (1965) and Hempel (1965) both point to paradoxes inherent in standard accounts of confirmation. Recent attempts at explaining how observations can serve to confirm a scientific theory are discussed in section 4 below.

The standard starting point for a non-inductive analysis of the logic of confirmation is known as the Hypothetico-Deductive (H-D) method. In its simplest form, a sentence of a theory which expresses some hypothesis is confirmed by its true consequences. As noted in section 2 , this method had been advanced by Whewell in the 19 th century, as well as Nicod (1924) and others in the 20 th century. Often, Hempel’s (1966) description of the H-D method, illustrated by the case of Semmelweiss’ inferential procedures in establishing the cause of childbed fever, has been presented as a key account of H-D as well as a foil for criticism of the H-D account of confirmation (see, for example, Lipton’s (2004) discussion of inference to the best explanation; also the entry on confirmation ). Hempel described Semmelsweiss’ procedure as examining various hypotheses explaining the cause of childbed fever. Some hypotheses conflicted with observable facts and could be rejected as false immediately. Others needed to be tested experimentally by deducing which observable events should follow if the hypothesis were true (what Hempel called the test implications of the hypothesis), then conducting an experiment and observing whether or not the test implications occurred. If the experiment showed the test implication to be false, the hypothesis could be rejected. If the experiment showed the test implications to be true, however, this did not prove the hypothesis true. The confirmation of a test implication does not verify a hypothesis, though Hempel did allow that “it provides at least some support, some corroboration or confirmation for it” (Hempel 1966: 8). The degree of this support then depends on the quantity, variety and precision of the supporting evidence.

Another approach that took off from the difficulties with inductive inference was Karl Popper’s critical rationalism or falsificationism (Popper 1959, 1963). Falsification is deductive and similar to H-D in that it involves scientists deducing observational consequences from the hypothesis under test. For Popper, however, the important point was not the degree of confirmation that successful prediction offered to a hypothesis. The crucial thing was the logical asymmetry between confirmation, based on inductive inference, and falsification, which can be based on a deductive inference. (This simple opposition was later questioned, by Lakatos, among others. See the entry on historicist theories of scientific rationality. )

Popper stressed that, regardless of the amount of confirming evidence, we can never be certain that a hypothesis is true without committing the fallacy of affirming the consequent. Instead, Popper introduced the notion of corroboration as a measure for how well a theory or hypothesis has survived previous testing—but without implying that this is also a measure for the probability that it is true.

Popper was also motivated by his doubts about the scientific status of theories like the Marxist theory of history or psycho-analysis, and so wanted to demarcate between science and pseudo-science. Popper saw this as an importantly different distinction than demarcating science from metaphysics. The latter demarcation was the primary concern of many logical empiricists. Popper used the idea of falsification to draw a line instead between pseudo and proper science. Science was science because its method involved subjecting theories to rigorous tests which offered a high probability of failing and thus refuting the theory.

A commitment to the risk of failure was important. Avoiding falsification could be done all too easily. If a consequence of a theory is inconsistent with observations, an exception can be added by introducing auxiliary hypotheses designed explicitly to save the theory, so-called ad hoc modifications. This Popper saw done in pseudo-science where ad hoc theories appeared capable of explaining anything in their field of application. In contrast, science is risky. If observations showed the predictions from a theory to be wrong, the theory would be refuted. Hence, scientific hypotheses must be falsifiable. Not only must there exist some possible observation statement which could falsify the hypothesis or theory, were it observed, (Popper called these the hypothesis’ potential falsifiers) it is crucial to the Popperian scientific method that such falsifications be sincerely attempted on a regular basis.

The more potential falsifiers of a hypothesis, the more falsifiable it would be, and the more the hypothesis claimed. Conversely, hypotheses without falsifiers claimed very little or nothing at all. Originally, Popper thought that this meant the introduction of ad hoc hypotheses only to save a theory should not be countenanced as good scientific method. These would undermine the falsifiabililty of a theory. However, Popper later came to recognize that the introduction of modifications (immunizations, he called them) was often an important part of scientific development. Responding to surprising or apparently falsifying observations often generated important new scientific insights. Popper’s own example was the observed motion of Uranus which originally did not agree with Newtonian predictions. The ad hoc hypothesis of an outer planet explained the disagreement and led to further falsifiable predictions. Popper sought to reconcile the view by blurring the distinction between falsifiable and not falsifiable, and speaking instead of degrees of testability (Popper 1985: 41f.).

From the 1960s on, sustained meta-methodological criticism emerged that drove philosophical focus away from scientific method. A brief look at those criticisms follows, with recommendations for further reading at the end of the entry.

Thomas Kuhn’s The Structure of Scientific Revolutions (1962) begins with a well-known shot across the bow for philosophers of science:

History, if viewed as a repository for more than anecdote or chronology, could produce a decisive transformation in the image of science by which we are now possessed. (1962: 1)

The image Kuhn thought needed transforming was the a-historical, rational reconstruction sought by many of the Logical Positivists, though Carnap and other positivists were actually quite sympathetic to Kuhn’s views. (See the entry on the Vienna Circle .) Kuhn shares with other of his contemporaries, such as Feyerabend and Lakatos, a commitment to a more empirical approach to philosophy of science. Namely, the history of science provides important data, and necessary checks, for philosophy of science, including any theory of scientific method.

The history of science reveals, according to Kuhn, that scientific development occurs in alternating phases. During normal science, the members of the scientific community adhere to the paradigm in place. Their commitment to the paradigm means a commitment to the puzzles to be solved and the acceptable ways of solving them. Confidence in the paradigm remains so long as steady progress is made in solving the shared puzzles. Method in this normal phase operates within a disciplinary matrix (Kuhn’s later concept of a paradigm) which includes standards for problem solving, and defines the range of problems to which the method should be applied. An important part of a disciplinary matrix is the set of values which provide the norms and aims for scientific method. The main values that Kuhn identifies are prediction, problem solving, simplicity, consistency, and plausibility.

An important by-product of normal science is the accumulation of puzzles which cannot be solved with resources of the current paradigm. Once accumulation of these anomalies has reached some critical mass, it can trigger a communal shift to a new paradigm and a new phase of normal science. Importantly, the values that provide the norms and aims for scientific method may have transformed in the meantime. Method may therefore be relative to discipline, time or place

Feyerabend also identified the aims of science as progress, but argued that any methodological prescription would only stifle that progress (Feyerabend 1988). His arguments are grounded in re-examining accepted “myths” about the history of science. Heroes of science, like Galileo, are shown to be just as reliant on rhetoric and persuasion as they are on reason and demonstration. Others, like Aristotle, are shown to be far more reasonable and far-reaching in their outlooks then they are given credit for. As a consequence, the only rule that could provide what he took to be sufficient freedom was the vacuous “anything goes”. More generally, even the methodological restriction that science is the best way to pursue knowledge, and to increase knowledge, is too restrictive. Feyerabend suggested instead that science might, in fact, be a threat to a free society, because it and its myth had become so dominant (Feyerabend 1978).

An even more fundamental kind of criticism was offered by several sociologists of science from the 1970s onwards who rejected the methodology of providing philosophical accounts for the rational development of science and sociological accounts of the irrational mistakes. Instead, they adhered to a symmetry thesis on which any causal explanation of how scientific knowledge is established needs to be symmetrical in explaining truth and falsity, rationality and irrationality, success and mistakes, by the same causal factors (see, e.g., Barnes and Bloor 1982, Bloor 1991). Movements in the Sociology of Science, like the Strong Programme, or in the social dimensions and causes of knowledge more generally led to extended and close examination of detailed case studies in contemporary science and its history. (See the entries on the social dimensions of scientific knowledge and social epistemology .) Well-known examinations by Latour and Woolgar (1979/1986), Knorr-Cetina (1981), Pickering (1984), Shapin and Schaffer (1985) seem to bear out that it was social ideologies (on a macro-scale) or individual interactions and circumstances (on a micro-scale) which were the primary causal factors in determining which beliefs gained the status of scientific knowledge. As they saw it therefore, explanatory appeals to scientific method were not empirically grounded.

A late, and largely unexpected, criticism of scientific method came from within science itself. Beginning in the early 2000s, a number of scientists attempting to replicate the results of published experiments could not do so. There may be close conceptual connection between reproducibility and method. For example, if reproducibility means that the same scientific methods ought to produce the same result, and all scientific results ought to be reproducible, then whatever it takes to reproduce a scientific result ought to be called scientific method. Space limits us to the observation that, insofar as reproducibility is a desired outcome of proper scientific method, it is not strictly a part of scientific method. (See the entry on reproducibility of scientific results .)

By the close of the 20 th century the search for the scientific method was flagging. Nola and Sankey (2000b) could introduce their volume on method by remarking that “For some, the whole idea of a theory of scientific method is yester-year’s debate …”.

Despite the many difficulties that philosophers encountered in trying to providing a clear methodology of conformation (or refutation), still important progress has been made on understanding how observation can provide evidence for a given theory. Work in statistics has been crucial for understanding how theories can be tested empirically, and in recent decades a huge literature has developed that attempts to recast confirmation in Bayesian terms. Here these developments can be covered only briefly, and we refer to the entry on confirmation for further details and references.

Statistics has come to play an increasingly important role in the methodology of the experimental sciences from the 19 th century onwards. At that time, statistics and probability theory took on a methodological role as an analysis of inductive inference, and attempts to ground the rationality of induction in the axioms of probability theory have continued throughout the 20 th century and in to the present. Developments in the theory of statistics itself, meanwhile, have had a direct and immense influence on the experimental method, including methods for measuring the uncertainty of observations such as the Method of Least Squares developed by Legendre and Gauss in the early 19 th century, criteria for the rejection of outliers proposed by Peirce by the mid-19 th century, and the significance tests developed by Gosset (a.k.a. “Student”), Fisher, Neyman & Pearson and others in the 1920s and 1930s (see, e.g., Swijtink 1987 for a brief historical overview; and also the entry on C.S. Peirce ).

These developments within statistics then in turn led to a reflective discussion among both statisticians and philosophers of science on how to perceive the process of hypothesis testing: whether it was a rigorous statistical inference that could provide a numerical expression of the degree of confidence in the tested hypothesis, or if it should be seen as a decision between different courses of actions that also involved a value component. This led to a major controversy among Fisher on the one side and Neyman and Pearson on the other (see especially Fisher 1955, Neyman 1956 and Pearson 1955, and for analyses of the controversy, e.g., Howie 2002, Marks 2000, Lenhard 2006). On Fisher’s view, hypothesis testing was a methodology for when to accept or reject a statistical hypothesis, namely that a hypothesis should be rejected by evidence if this evidence would be unlikely relative to other possible outcomes, given the hypothesis were true. In contrast, on Neyman and Pearson’s view, the consequence of error also had to play a role when deciding between hypotheses. Introducing the distinction between the error of rejecting a true hypothesis (type I error) and accepting a false hypothesis (type II error), they argued that it depends on the consequences of the error to decide whether it is more important to avoid rejecting a true hypothesis or accepting a false one. Hence, Fisher aimed for a theory of inductive inference that enabled a numerical expression of confidence in a hypothesis. To him, the important point was the search for truth, not utility. In contrast, the Neyman-Pearson approach provided a strategy of inductive behaviour for deciding between different courses of action. Here, the important point was not whether a hypothesis was true, but whether one should act as if it was.

Similar discussions are found in the philosophical literature. On the one side, Churchman (1948) and Rudner (1953) argued that because scientific hypotheses can never be completely verified, a complete analysis of the methods of scientific inference includes ethical judgments in which the scientists must decide whether the evidence is sufficiently strong or that the probability is sufficiently high to warrant the acceptance of the hypothesis, which again will depend on the importance of making a mistake in accepting or rejecting the hypothesis. Others, such as Jeffrey (1956) and Levi (1960) disagreed and instead defended a value-neutral view of science on which scientists should bracket their attitudes, preferences, temperament, and values when assessing the correctness of their inferences. For more details on this value-free ideal in the philosophy of science and its historical development, see Douglas (2009) and Howard (2003). For a broad set of case studies examining the role of values in science, see e.g. Elliott & Richards 2017.

In recent decades, philosophical discussions of the evaluation of probabilistic hypotheses by statistical inference have largely focused on Bayesianism that understands probability as a measure of a person’s degree of belief in an event, given the available information, and frequentism that instead understands probability as a long-run frequency of a repeatable event. Hence, for Bayesians probabilities refer to a state of knowledge, whereas for frequentists probabilities refer to frequencies of events (see, e.g., Sober 2008, chapter 1 for a detailed introduction to Bayesianism and frequentism as well as to likelihoodism). Bayesianism aims at providing a quantifiable, algorithmic representation of belief revision, where belief revision is a function of prior beliefs (i.e., background knowledge) and incoming evidence. Bayesianism employs a rule based on Bayes’ theorem, a theorem of the probability calculus which relates conditional probabilities. The probability that a particular hypothesis is true is interpreted as a degree of belief, or credence, of the scientist. There will also be a probability and a degree of belief that a hypothesis will be true conditional on a piece of evidence (an observation, say) being true. Bayesianism proscribes that it is rational for the scientist to update their belief in the hypothesis to that conditional probability should it turn out that the evidence is, in fact, observed (see, e.g., Sprenger & Hartmann 2019 for a comprehensive treatment of Bayesian philosophy of science). Originating in the work of Neyman and Person, frequentism aims at providing the tools for reducing long-run error rates, such as the error-statistical approach developed by Mayo (1996) that focuses on how experimenters can avoid both type I and type II errors by building up a repertoire of procedures that detect errors if and only if they are present. Both Bayesianism and frequentism have developed over time, they are interpreted in different ways by its various proponents, and their relations to previous criticism to attempts at defining scientific method are seen differently by proponents and critics. The literature, surveys, reviews and criticism in this area are vast and the reader is referred to the entries on Bayesian epistemology and confirmation .

5. Method in Practice

Attention to scientific practice, as we have seen, is not itself new. However, the turn to practice in the philosophy of science of late can be seen as a correction to the pessimism with respect to method in philosophy of science in later parts of the 20 th century, and as an attempted reconciliation between sociological and rationalist explanations of scientific knowledge. Much of this work sees method as detailed and context specific problem-solving procedures, and methodological analyses to be at the same time descriptive, critical and advisory (see Nickles 1987 for an exposition of this view). The following section contains a survey of some of the practice focuses. In this section we turn fully to topics rather than chronology.

A problem with the distinction between the contexts of discovery and justification that figured so prominently in philosophy of science in the first half of the 20 th century (see section 2 ) is that no such distinction can be clearly seen in scientific activity (see Arabatzis 2006). Thus, in recent decades, it has been recognized that study of conceptual innovation and change should not be confined to psychology and sociology of science, but are also important aspects of scientific practice which philosophy of science should address (see also the entry on scientific discovery ). Looking for the practices that drive conceptual innovation has led philosophers to examine both the reasoning practices of scientists and the wide realm of experimental practices that are not directed narrowly at testing hypotheses, that is, exploratory experimentation.

Examining the reasoning practices of historical and contemporary scientists, Nersessian (2008) has argued that new scientific concepts are constructed as solutions to specific problems by systematic reasoning, and that of analogy, visual representation and thought-experimentation are among the important reasoning practices employed. These ubiquitous forms of reasoning are reliable—but also fallible—methods of conceptual development and change. On her account, model-based reasoning consists of cycles of construction, simulation, evaluation and adaption of models that serve as interim interpretations of the target problem to be solved. Often, this process will lead to modifications or extensions, and a new cycle of simulation and evaluation. However, Nersessian also emphasizes that

creative model-based reasoning cannot be applied as a simple recipe, is not always productive of solutions, and even its most exemplary usages can lead to incorrect solutions. (Nersessian 2008: 11)

Thus, while on the one hand she agrees with many previous philosophers that there is no logic of discovery, discoveries can derive from reasoned processes, such that a large and integral part of scientific practice is

the creation of concepts through which to comprehend, structure, and communicate about physical phenomena …. (Nersessian 1987: 11)

Similarly, work on heuristics for discovery and theory construction by scholars such as Darden (1991) and Bechtel & Richardson (1993) present science as problem solving and investigate scientific problem solving as a special case of problem-solving in general. Drawing largely on cases from the biological sciences, much of their focus has been on reasoning strategies for the generation, evaluation, and revision of mechanistic explanations of complex systems.

Addressing another aspect of the context distinction, namely the traditional view that the primary role of experiments is to test theoretical hypotheses according to the H-D model, other philosophers of science have argued for additional roles that experiments can play. The notion of exploratory experimentation was introduced to describe experiments driven by the desire to obtain empirical regularities and to develop concepts and classifications in which these regularities can be described (Steinle 1997, 2002; Burian 1997; Waters 2007)). However the difference between theory driven experimentation and exploratory experimentation should not be seen as a sharp distinction. Theory driven experiments are not always directed at testing hypothesis, but may also be directed at various kinds of fact-gathering, such as determining numerical parameters. Vice versa , exploratory experiments are usually informed by theory in various ways and are therefore not theory-free. Instead, in exploratory experiments phenomena are investigated without first limiting the possible outcomes of the experiment on the basis of extant theory about the phenomena.

The development of high throughput instrumentation in molecular biology and neighbouring fields has given rise to a special type of exploratory experimentation that collects and analyses very large amounts of data, and these new ‘omics’ disciplines are often said to represent a break with the ideal of hypothesis-driven science (Burian 2007; Elliott 2007; Waters 2007; O’Malley 2007) and instead described as data-driven research (Leonelli 2012; Strasser 2012) or as a special kind of “convenience experimentation” in which many experiments are done simply because they are extraordinarily convenient to perform (Krohs 2012).

5.2 Computer methods and ‘new ways’ of doing science

The field of omics just described is possible because of the ability of computers to process, in a reasonable amount of time, the huge quantities of data required. Computers allow for more elaborate experimentation (higher speed, better filtering, more variables, sophisticated coordination and control), but also, through modelling and simulations, might constitute a form of experimentation themselves. Here, too, we can pose a version of the general question of method versus practice: does the practice of using computers fundamentally change scientific method, or merely provide a more efficient means of implementing standard methods?

Because computers can be used to automate measurements, quantifications, calculations, and statistical analyses where, for practical reasons, these operations cannot be otherwise carried out, many of the steps involved in reaching a conclusion on the basis of an experiment are now made inside a “black box”, without the direct involvement or awareness of a human. This has epistemological implications, regarding what we can know, and how we can know it. To have confidence in the results, computer methods are therefore subjected to tests of verification and validation.

The distinction between verification and validation is easiest to characterize in the case of computer simulations. In a typical computer simulation scenario computers are used to numerically integrate differential equations for which no analytic solution is available. The equations are part of the model the scientist uses to represent a phenomenon or system under investigation. Verifying a computer simulation means checking that the equations of the model are being correctly approximated. Validating a simulation means checking that the equations of the model are adequate for the inferences one wants to make on the basis of that model.

A number of issues related to computer simulations have been raised. The identification of validity and verification as the testing methods has been criticized. Oreskes et al. (1994) raise concerns that “validiation”, because it suggests deductive inference, might lead to over-confidence in the results of simulations. The distinction itself is probably too clean, since actual practice in the testing of simulations mixes and moves back and forth between the two (Weissart 1997; Parker 2008a; Winsberg 2010). Computer simulations do seem to have a non-inductive character, given that the principles by which they operate are built in by the programmers, and any results of the simulation follow from those in-built principles in such a way that those results could, in principle, be deduced from the program code and its inputs. The status of simulations as experiments has therefore been examined (Kaufmann and Smarr 1993; Humphreys 1995; Hughes 1999; Norton and Suppe 2001). This literature considers the epistemology of these experiments: what we can learn by simulation, and also the kinds of justifications which can be given in applying that knowledge to the “real” world. (Mayo 1996; Parker 2008b). As pointed out, part of the advantage of computer simulation derives from the fact that huge numbers of calculations can be carried out without requiring direct observation by the experimenter/​simulator. At the same time, many of these calculations are approximations to the calculations which would be performed first-hand in an ideal situation. Both factors introduce uncertainties into the inferences drawn from what is observed in the simulation.

For many of the reasons described above, computer simulations do not seem to belong clearly to either the experimental or theoretical domain. Rather, they seem to crucially involve aspects of both. This has led some authors, such as Fox Keller (2003: 200) to argue that we ought to consider computer simulation a “qualitatively different way of doing science”. The literature in general tends to follow Kaufmann and Smarr (1993) in referring to computer simulation as a “third way” for scientific methodology (theoretical reasoning and experimental practice are the first two ways.). It should also be noted that the debates around these issues have tended to focus on the form of computer simulation typical in the physical sciences, where models are based on dynamical equations. Other forms of simulation might not have the same problems, or have problems of their own (see the entry on computer simulations in science ).

In recent years, the rapid development of machine learning techniques has prompted some scholars to suggest that the scientific method has become “obsolete” (Anderson 2008, Carrol and Goodstein 2009). This has resulted in an intense debate on the relative merit of data-driven and hypothesis-driven research (for samples, see e.g. Mazzocchi 2015 or Succi and Coveney 2018). For a detailed treatment of this topic, we refer to the entry scientific research and big data .

6. Discourse on scientific method

Despite philosophical disagreements, the idea of the scientific method still figures prominently in contemporary discourse on many different topics, both within science and in society at large. Often, reference to scientific method is used in ways that convey either the legend of a single, universal method characteristic of all science, or grants to a particular method or set of methods privilege as a special ‘gold standard’, often with reference to particular philosophers to vindicate the claims. Discourse on scientific method also typically arises when there is a need to distinguish between science and other activities, or for justifying the special status conveyed to science. In these areas, the philosophical attempts at identifying a set of methods characteristic for scientific endeavors are closely related to the philosophy of science’s classical problem of demarcation (see the entry on science and pseudo-science ) and to the philosophical analysis of the social dimension of scientific knowledge and the role of science in democratic society.

One of the settings in which the legend of a single, universal scientific method has been particularly strong is science education (see, e.g., Bauer 1992; McComas 1996; Wivagg & Allchin 2002). [ 5 ] Often, ‘the scientific method’ is presented in textbooks and educational web pages as a fixed four or five step procedure starting from observations and description of a phenomenon and progressing over formulation of a hypothesis which explains the phenomenon, designing and conducting experiments to test the hypothesis, analyzing the results, and ending with drawing a conclusion. Such references to a universal scientific method can be found in educational material at all levels of science education (Blachowicz 2009), and numerous studies have shown that the idea of a general and universal scientific method often form part of both students’ and teachers’ conception of science (see, e.g., Aikenhead 1987; Osborne et al. 2003). In response, it has been argued that science education need to focus more on teaching about the nature of science, although views have differed on whether this is best done through student-led investigations, contemporary cases, or historical cases (Allchin, Andersen & Nielsen 2014)

Although occasionally phrased with reference to the H-D method, important historical roots of the legend in science education of a single, universal scientific method are the American philosopher and psychologist Dewey’s account of inquiry in How We Think (1910) and the British mathematician Karl Pearson’s account of science in Grammar of Science (1892). On Dewey’s account, inquiry is divided into the five steps of

(i) a felt difficulty, (ii) its location and definition, (iii) suggestion of a possible solution, (iv) development by reasoning of the bearing of the suggestions, (v) further observation and experiment leading to its acceptance or rejection. (Dewey 1910: 72)

Similarly, on Pearson’s account, scientific investigations start with measurement of data and observation of their correction and sequence from which scientific laws can be discovered with the aid of creative imagination. These laws have to be subject to criticism, and their final acceptance will have equal validity for “all normally constituted minds”. Both Dewey’s and Pearson’s accounts should be seen as generalized abstractions of inquiry and not restricted to the realm of science—although both Dewey and Pearson referred to their respective accounts as ‘the scientific method’.

Occasionally, scientists make sweeping statements about a simple and distinct scientific method, as exemplified by Feynman’s simplified version of a conjectures and refutations method presented, for example, in the last of his 1964 Cornell Messenger lectures. [ 6 ] However, just as often scientists have come to the same conclusion as recent philosophy of science that there is not any unique, easily described scientific method. For example, the physicist and Nobel Laureate Weinberg described in the paper “The Methods of Science … And Those By Which We Live” (1995) how

The fact that the standards of scientific success shift with time does not only make the philosophy of science difficult; it also raises problems for the public understanding of science. We do not have a fixed scientific method to rally around and defend. (1995: 8)

Interview studies with scientists on their conception of method shows that scientists often find it hard to figure out whether available evidence confirms their hypothesis, and that there are no direct translations between general ideas about method and specific strategies to guide how research is conducted (Schickore & Hangel 2019, Hangel & Schickore 2017)

Reference to the scientific method has also often been used to argue for the scientific nature or special status of a particular activity. Philosophical positions that argue for a simple and unique scientific method as a criterion of demarcation, such as Popperian falsification, have often attracted practitioners who felt that they had a need to defend their domain of practice. For example, references to conjectures and refutation as the scientific method are abundant in much of the literature on complementary and alternative medicine (CAM)—alongside the competing position that CAM, as an alternative to conventional biomedicine, needs to develop its own methodology different from that of science.

Also within mainstream science, reference to the scientific method is used in arguments regarding the internal hierarchy of disciplines and domains. A frequently seen argument is that research based on the H-D method is superior to research based on induction from observations because in deductive inferences the conclusion follows necessarily from the premises. (See, e.g., Parascandola 1998 for an analysis of how this argument has been made to downgrade epidemiology compared to the laboratory sciences.) Similarly, based on an examination of the practices of major funding institutions such as the National Institutes of Health (NIH), the National Science Foundation (NSF) and the Biomedical Sciences Research Practices (BBSRC) in the UK, O’Malley et al. (2009) have argued that funding agencies seem to have a tendency to adhere to the view that the primary activity of science is to test hypotheses, while descriptive and exploratory research is seen as merely preparatory activities that are valuable only insofar as they fuel hypothesis-driven research.

In some areas of science, scholarly publications are structured in a way that may convey the impression of a neat and linear process of inquiry from stating a question, devising the methods by which to answer it, collecting the data, to drawing a conclusion from the analysis of data. For example, the codified format of publications in most biomedical journals known as the IMRAD format (Introduction, Method, Results, Analysis, Discussion) is explicitly described by the journal editors as “not an arbitrary publication format but rather a direct reflection of the process of scientific discovery” (see the so-called “Vancouver Recommendations”, ICMJE 2013: 11). However, scientific publications do not in general reflect the process by which the reported scientific results were produced. For example, under the provocative title “Is the scientific paper a fraud?”, Medawar argued that scientific papers generally misrepresent how the results have been produced (Medawar 1963/1996). Similar views have been advanced by philosophers, historians and sociologists of science (Gilbert 1976; Holmes 1987; Knorr-Cetina 1981; Schickore 2008; Suppe 1998) who have argued that scientists’ experimental practices are messy and often do not follow any recognizable pattern. Publications of research results, they argue, are retrospective reconstructions of these activities that often do not preserve the temporal order or the logic of these activities, but are instead often constructed in order to screen off potential criticism (see Schickore 2008 for a review of this work).

Philosophical positions on the scientific method have also made it into the court room, especially in the US where judges have drawn on philosophy of science in deciding when to confer special status to scientific expert testimony. A key case is Daubert vs Merrell Dow Pharmaceuticals (92–102, 509 U.S. 579, 1993). In this case, the Supreme Court argued in its 1993 ruling that trial judges must ensure that expert testimony is reliable, and that in doing this the court must look at the expert’s methodology to determine whether the proffered evidence is actually scientific knowledge. Further, referring to works of Popper and Hempel the court stated that

ordinarily, a key question to be answered in determining whether a theory or technique is scientific knowledge … is whether it can be (and has been) tested. (Justice Blackmun, Daubert v. Merrell Dow Pharmaceuticals; see Other Internet Resources for a link to the opinion)

But as argued by Haack (2005a,b, 2010) and by Foster & Hubner (1999), by equating the question of whether a piece of testimony is reliable with the question whether it is scientific as indicated by a special methodology, the court was producing an inconsistent mixture of Popper’s and Hempel’s philosophies, and this has later led to considerable confusion in subsequent case rulings that drew on the Daubert case (see Haack 2010 for a detailed exposition).

The difficulties around identifying the methods of science are also reflected in the difficulties of identifying scientific misconduct in the form of improper application of the method or methods of science. One of the first and most influential attempts at defining misconduct in science was the US definition from 1989 that defined misconduct as

fabrication, falsification, plagiarism, or other practices that seriously deviate from those that are commonly accepted within the scientific community . (Code of Federal Regulations, part 50, subpart A., August 8, 1989, italics added)

However, the “other practices that seriously deviate” clause was heavily criticized because it could be used to suppress creative or novel science. For example, the National Academy of Science stated in their report Responsible Science (1992) that it

wishes to discourage the possibility that a misconduct complaint could be lodged against scientists based solely on their use of novel or unorthodox research methods. (NAS: 27)

This clause was therefore later removed from the definition. For an entry into the key philosophical literature on conduct in science, see Shamoo & Resnick (2009).

The question of the source of the success of science has been at the core of philosophy since the beginning of modern science. If viewed as a matter of epistemology more generally, scientific method is a part of the entire history of philosophy. Over that time, science and whatever methods its practitioners may employ have changed dramatically. Today, many philosophers have taken up the banners of pluralism or of practice to focus on what are, in effect, fine-grained and contextually limited examinations of scientific method. Others hope to shift perspectives in order to provide a renewed general account of what characterizes the activity we call science.

One such perspective has been offered recently by Hoyningen-Huene (2008, 2013), who argues from the history of philosophy of science that after three lengthy phases of characterizing science by its method, we are now in a phase where the belief in the existence of a positive scientific method has eroded and what has been left to characterize science is only its fallibility. First was a phase from Plato and Aristotle up until the 17 th century where the specificity of scientific knowledge was seen in its absolute certainty established by proof from evident axioms; next was a phase up to the mid-19 th century in which the means to establish the certainty of scientific knowledge had been generalized to include inductive procedures as well. In the third phase, which lasted until the last decades of the 20 th century, it was recognized that empirical knowledge was fallible, but it was still granted a special status due to its distinctive mode of production. But now in the fourth phase, according to Hoyningen-Huene, historical and philosophical studies have shown how “scientific methods with the characteristics as posited in the second and third phase do not exist” (2008: 168) and there is no longer any consensus among philosophers and historians of science about the nature of science. For Hoyningen-Huene, this is too negative a stance, and he therefore urges the question about the nature of science anew. His own answer to this question is that “scientific knowledge differs from other kinds of knowledge, especially everyday knowledge, primarily by being more systematic” (Hoyningen-Huene 2013: 14). Systematicity can have several different dimensions: among them are more systematic descriptions, explanations, predictions, defense of knowledge claims, epistemic connectedness, ideal of completeness, knowledge generation, representation of knowledge and critical discourse. Hence, what characterizes science is the greater care in excluding possible alternative explanations, the more detailed elaboration with respect to data on which predictions are based, the greater care in detecting and eliminating sources of error, the more articulate connections to other pieces of knowledge, etc. On this position, what characterizes science is not that the methods employed are unique to science, but that the methods are more carefully employed.

Another, similar approach has been offered by Haack (2003). She sets off, similar to Hoyningen-Huene, from a dissatisfaction with the recent clash between what she calls Old Deferentialism and New Cynicism. The Old Deferentialist position is that science progressed inductively by accumulating true theories confirmed by empirical evidence or deductively by testing conjectures against basic statements; while the New Cynics position is that science has no epistemic authority and no uniquely rational method and is merely just politics. Haack insists that contrary to the views of the New Cynics, there are objective epistemic standards, and there is something epistemologically special about science, even though the Old Deferentialists pictured this in a wrong way. Instead, she offers a new Critical Commonsensist account on which standards of good, strong, supportive evidence and well-conducted, honest, thorough and imaginative inquiry are not exclusive to the sciences, but the standards by which we judge all inquirers. In this sense, science does not differ in kind from other kinds of inquiry, but it may differ in the degree to which it requires broad and detailed background knowledge and a familiarity with a technical vocabulary that only specialists may possess.

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• Blackmun opinion , in Daubert v. Merrell Dow Pharmaceuticals (92–102), 509 U.S. 579 (1993).
• Scientific Method at philpapers. Darrell Rowbottom (ed.).
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How to Write an Expository Essay

Published by Grace Graffin at August 17th, 2021 , Revised On July 26, 2023

Expository means “to describe or explain something” . It is related to the words ‘exposition’, ‘expound’, and ‘expose’ – to explain or reveal the meaning, to lay open, speak one’s mind.

Whenever there is a need to gather research and describe an idea, a  topic , or a process clearly and logically, it is done in the form of an expository  essay .

An expository essay requires the writer to take a balanced approach to the subject matter rather than justifying a particular point of view.

Expository essays are assigned to students to evaluate their subject knowledge and composition skills. When compared with  argumentative essays , they involve a lot less research.

Definition of Expository Essay

“The expository essay is the  type of essay  that involves an investigation of an idea or topic, appraises relevant supporting evidence material, and presents an argument in a clear and concise manner. ”

When to Write an Expository Essay

Your school or university could assign an expository essay to you as coursework or as part of an online exam.

However, the guidelines may or may not clearly state that your assignment is an expository essay. If that is the case, then look for keywords like ‘explain’, ‘describe’, ‘define’, etc., to be sure that what has been asked for is an expository essay.

You might even be asked to explain and emphasise a particular concept or term. Writing a simple definition will not be enough because you will be expected to explore the ideas in detail.

Writing an Expository Essay

An expository essay should not be based on your  personal  experiences and opinions. It rather takes an objective approach. You will be expected to explain the topic in a balanced way without any personal bias.

Make sure to avoid the first and second person (“I” and “You”) when writing an expository essay.

How to Structure an Expository Essay

The  structure  and format of your expository essay assignment will depend on your school’s guidelines and the topic you are investigating. However, it is always a good idea to develop an outline for your  essay  before starting to work.

The Five-Paragraph Essay Writing Approach

An expository essay will require you to take the five-paragraph essay approach: an  introductory paragraph , a main body paragraph , and a concluding paragraph . This is often referred to as the hamburger style of the essay because, like a hamburger, it contains five main parts: the introduction and conclusion being the bun that encapsulates everything.

Rationale and Thesis Statement

Start your essay  with a rationale and thesis, also known as the  thesis statement , so your readers know what you set out to achieve in your expository essay assignment. Ensure the thesis statement is narrow enough to follow the guidelines in the assignment brief. If the thesis statement is weak and too broad, you will struggle to produce a flawless expository essay.

The Framework

Construct a framework, so you know what elements will constitute the basis of your essay.

Expository Essay Introduction

Like other  essay types , an expository essay begins with an  introduction , including a hook, background to the topic, and a thesis statement. Once you have grabbed the readers’ interest, it will be easier to get them to read the remaining essay.

Frequently Asked Questions

Will i need the skill of expository writing after i finish my studies.

It depends on what you are studying for. While you might or might not write any more expository essays after your formal education has ended, the skill will be very useful in certain careers, such as business reports, journalism, and in scientific and technical writing.

How does an expository essay differ from an argumentative essay?

An argumentative essay is usually longer and requires more research. It starts with a claim about something that will need supporting evidence. And both sides of the argument need to be discussed. In an expository essay, there is no requirement to make an original argument and defend/support it.

What is the purpose of expository essays?

This style of essay is necessary when you have to showcase your knowledge on a given subject, or your ability to gather research on one and present your findings.

How long is an expository essay?

There is no fixed length but an expository essay could be part of an exam, in which case it might only be 1,000 words or less. They are usually shorter than argumentative essays . It can depend on the subject under discussion. You will likely be given instructions on the required word count.

Are there different types of expository essay?

There are six different types of expository essay, each with a different purpose.

The six types are:

Process essay – describing a task, a method, how to complete something Cause and effect essay – why something happened and its effects Problem-solution essay – provide analysis of problems and their solutions Compare and contrast essay – describe the similarities and differences between two subjects Definition essay – define the topic in detail and explain the how, what, and why Classification essay – separate the topic’s categories and define them in detail

When you are assigned your essay, you should be able to distinguish which of these approaches you are required to take.

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Perspective: Dimensions of the scientific method

Eberhard o. voit.

Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America

The scientific method has been guiding biological research for a long time. It not only prescribes the order and types of activities that give a scientific study validity and a stamp of approval but also has substantially shaped how we collectively think about the endeavor of investigating nature. The advent of high-throughput data generation, data mining, and advanced computational modeling has thrown the formerly undisputed, monolithic status of the scientific method into turmoil. On the one hand, the new approaches are clearly successful and expect the same acceptance as the traditional methods, but on the other hand, they replace much of the hypothesis-driven reasoning with inductive argumentation, which philosophers of science consider problematic. Intrigued by the enormous wealth of data and the power of machine learning, some scientists have even argued that significant correlations within datasets could make the entire quest for causation obsolete. Many of these issues have been passionately debated during the past two decades, often with scant agreement. It is proffered here that hypothesis-driven, data-mining–inspired, and “allochthonous” knowledge acquisition, based on mathematical and computational models, are vectors spanning a 3D space of an expanded scientific method. The combination of methods within this space will most certainly shape our thinking about nature, with implications for experimental design, peer review and funding, sharing of result, education, medical diagnostics, and even questions of litigation.

The traditional scientific method: Hypothesis-driven deduction

Research is the undisputed core activity defining science. Without research, the advancement of scientific knowledge would come to a screeching halt. While it is evident that researchers look for new information or insights, the term “research” is somewhat puzzling. Never mind the prefix “re,” which simply means “coming back and doing it again and again,” the word “search” seems to suggest that the research process is somewhat haphazard, that not much of a strategy is involved in the process. One might argue that research a few hundred years ago had the character of hoping for enough luck to find something new. The alchemists come to mind in their quest to turn mercury or lead into gold, or to discover an elixir for eternal youth, through methods we nowadays consider laughable.

Today’s sciences, in stark contrast, are clearly different. Yes, we still try to find something new—and may need a good dose of luck—but the process is anything but unstructured. In fact, it is prescribed in such rigor that it has been given the widely known moniker “scientific method.” This scientific method has deep roots going back to Aristotle and Herophilus (approximately 300 BC), Avicenna and Alhazen (approximately 1,000 AD), Grosseteste and Robert Bacon (approximately 1,250 AD), and many others, but solidified and crystallized into the gold standard of quality research during the 17th and 18th centuries [ 1 – 7 ]. In particular, Sir Francis Bacon (1561–1626) and René Descartes (1596–1650) are often considered the founders of the scientific method, because they insisted on careful, systematic observations of high quality, rather than metaphysical speculations that were en vogue among the scholars of the time [ 1 , 8 ]. In contrast to their peers, they strove for objectivity and insisted that observations, rather than an investigator’s preconceived ideas or superstitions, should be the basis for formulating a research idea [ 7 , 9 ].

Bacon and his 19th century follower John Stuart Mill explicitly proposed gaining knowledge through inductive reasoning: Based on carefully recorded observations, or from data obtained in a well-planned experiment, generalized assertions were to be made about similar yet (so far) unobserved phenomena [ 7 ]. Expressed differently, inductive reasoning attempts to derive general principles or laws directly from empirical evidence [ 10 ]. An example is the 19th century epigram of the physician Rudolf Virchow, Omnis cellula e cellula . There is no proof that indeed “every cell derives from a cell,” but like Virchow, we have made the observation time and again and never encountered anything suggesting otherwise.

In contrast to induction, the widely accepted, traditional scientific method is based on formulating and testing hypotheses. From the results of these tests, a deduction is made whether the hypothesis is presumably true or false. This type of hypotheticodeductive reasoning goes back to William Whewell, William Stanley Jevons, and Charles Peirce in the 19th century [ 1 ]. By the 20th century, the deductive, hypothesis-based scientific method had become deeply ingrained in the scientific psyche, and it is now taught as early as middle school in order to teach students valid means of discovery [ 8 , 11 , 12 ]. The scientific method has not only guided most research studies but also fundamentally influenced how we think about the process of scientific discovery.

Alas, because biology has almost no general laws, deduction in the strictest sense is difficult. It may therefore be preferable to use the term abduction, which refers to the logical inference toward the most plausible explanation, given a set of observations, although this explanation cannot be proven and is not necessarily true.

Over the decades, the hypothesis-based scientific method did experience variations here and there, but its conceptual scaffold remained essentially unchanged ( Fig 1 ). Its key is a process that begins with the formulation of a hypothesis that is to be rigorously tested, either in the wet lab or computationally; nonadherence to this principle is seen as lacking rigor and can lead to irreproducible results [ 1 , 13 – 15 ].

The central concept of the traditional scientific method is a falsifiable hypothesis regarding some phenomenon of interest. This hypothesis is to be tested experimentally or computationally. The test results support or refute the hypothesis, triggering a new round of hypothesis formulation and testing.

Going further, the prominent philosopher of science Sir Karl Popper argued that a scientific hypothesis can never be verified but that it can be disproved by a single counterexample. He therefore demanded that scientific hypotheses had to be falsifiable, because otherwise, testing would be moot [ 16 , 17 ] (see also [ 18 ]). As Gillies put it, “successful theories are those that survive elimination through falsification” [ 19 ]. Kelley and Scott agreed to some degree but warned that complete insistence on falsifiability is too restrictive as it would mark many computational techniques, statistical hypothesis testing, and even Darwin’s theory of evolution as nonscientific [ 20 ].

While the hypothesis-based scientific method has been very successful, its exclusive reliance on deductive reasoning is dangerous because according to the so-called Duhem–Quine thesis, hypothesis testing always involves an unknown number of explicit or implicit assumptions, some of which may steer the researcher away from hypotheses that seem implausible, although they are, in fact, true [ 21 ]. According to Kuhn, this bias can obstruct the recognition of paradigm shifts [ 22 ], which require the rethinking of previously accepted “truths” and the development of radically new ideas [ 23 , 24 ]. The testing of simultaneous alternative hypotheses [ 25 – 27 ] ameliorates this problem to some degree but not entirely.

The traditional scientific method is often presented in discrete steps, but it should really be seen as a form of critical thinking, subject to review and independent validation [ 8 ]. It has proven very influential, not only by prescribing valid experimentation, but also for affecting the way we attempt to understand nature [ 18 ], for teaching [ 8 , 12 ], reporting, publishing, and otherwise sharing information [ 28 ], for peer review and the awarding of funds by research-supporting agencies [ 29 , 30 ], for medical diagnostics [ 7 ], and even in litigation [ 31 ].

A second dimension of the scientific method: Data-mining–inspired induction

A major shift in biological experimentation occurred with the–omics revolution of the early 21st century. All of a sudden, it became feasible to perform high-throughput experiments that generated thousands of measurements, typically characterizing the expression or abundances of very many—if not all—genes, proteins, metabolites, or other biological quantities in a sample.

The strategy of measuring large numbers of items in a nontargeted fashion is fundamentally different from the traditional scientific method and constitutes a new, second dimension of the scientific method. Instead of hypothesizing and testing whether gene X is up-regulated under some altered condition, the leading question becomes which of the thousands of genes in a sample are up- or down-regulated. This shift in focus elevates the data to the supreme role of revealing novel insights by themselves ( Fig 2 ). As an important, generic advantage over the traditional strategy, this second dimension is free of a researcher’s preconceived notions regarding the molecular mechanisms governing the phenomenon of interest, which are otherwise the key to formulating a hypothesis. The prominent biologists Patrick Brown and David Botstein commented that “the patterns of expression will often suffice to begin de novo discovery of potential gene functions” [ 32 ].

Data-driven research begins with an untargeted exploration, in which the data speak for themselves. Machine learning extracts patterns from the data, which suggest hypotheses that are to be tested in the lab or computationally.

This data-driven, discovery-generating approach is at once appealing and challenging. On the one hand, very many data are explored simultaneously and essentially without bias. On the other hand, the large datasets supporting this approach create a genuine challenge to understanding and interpreting the experimental results because the thousands of data points, often superimposed with a fair amount of noise, make it difficult to detect meaningful differences between sample and control. This situation can only be addressed with computational methods that first “clean” the data, for instance, through the statistically valid removal of outliers, and then use machine learning to identify statistically significant, distinguishing molecular profiles or signatures. In favorable cases, such signatures point to specific biological pathways, whereas other signatures defy direct explanation but may become the launch pad for follow-up investigations [ 33 ].

Today’s scientists are very familiar with this discovery-driven exploration of “what’s out there” and might consider it a quaint quirk of history that this strategy was at first widely chastised and ridiculed as a “fishing expedition” [ 30 , 34 ]. Strict traditionalists were outraged that rigor was leaving science with the new approach and that sufficient guidelines were unavailable to assure the validity and reproducibility of results [ 10 , 35 , 36 ].

From the view point of philosophy of science, this second dimension of the scientific method uses inductive reasoning and reflects Bacon’s idea that observations can and should dictate the research question to be investigated [ 1 , 7 ]. Allen [ 36 ] forcefully rejected this type of reasoning, stating “the thinking goes, we can now expect computer programs to derive significance, relevance and meaning from chunks of information, be they nucleotide sequences or gene expression profiles… In contrast with this view, many are convinced that no purely logical process can turn observation into understanding.” His conviction goes back to the 18th century philosopher David Hume and again to Popper, who identified as the overriding problem with inductive reasoning that it can never truly reveal causality, even if a phenomenon is observed time and again [ 16 , 17 , 37 , 38 ]. No number of observations, even if they always have the same result, can guard against an exception that would violate the generality of a law inferred from these observations [ 1 , 35 ]. Worse, Popper argued, through inference by induction, we cannot even know the probability of something being true [ 10 , 17 , 36 ].

Others argued that data-driven and hypothesis-driven research actually do not differ all that much in principle, as long as there is cycling between developing new ideas and testing them with care [ 27 ]. In fact, Kell and Oliver [ 34 ] maintained that the exclusive acceptance of hypothesis-driven programs misrepresents the complexities of biological knowledge generation. Similarly refuting the prominent rule of deduction, Platt [ 26 ] and Beard and Kushmerick [ 27 ] argued that repeated inductive reasoning, called strong inference, corresponds to a logically sound decision tree of disproving or refining hypotheses that can rapidly yield firm conclusions; nonetheless, Platt had to admit that inductive inference is not as certain as deduction, because it projects into the unknown. Lander compared the task of obtaining causality by induction to the problem of inferring the design of a microprocessor from input-output readings, which in a strict sense is impossible, because the microprocessor could be arbitrarily complicated; even so, inference often leads to novel insights and therefore is valuable [ 39 ].

An interesting special case of almost pure inductive reasoning is epidemiology, where hypothesis-driven reasoning is rare and instead, the fundamental question is whether data-based evidence is sufficient to associate health risks with specific causes [ 31 , 34 ].

Recent advances in machine learning and “big-data” mining have driven the use of inductive reasoning to unprecedented heights. As an example, machine learning can greatly assist in the discovery of patterns, for instance, in biological sequences [ 40 ]. Going a step further, a pithy article by Andersen [ 41 ] proffered that we may not need to look for causality or mechanistic explanations anymore if we just have enough correlation: “With enough data, the numbers speak for themselves, correlation replaces causation, and science can advance even without coherent models or unified theories.”

Of course, the proposal to abandon the quest for causality caused pushback on philosophical as well as mathematical grounds. Allen [ 10 , 35 ] considered the idea “absurd” that data analysis could enhance understanding in the absence of a hypothesis. He felt confident “that even the formidable combination of computing power with ease of access to data cannot produce a qualitative shift in the way that we do science: the making of hypotheses remains an indispensable component in the growth of knowledge” [ 36 ]. Succi and Coveney [ 42 ] refuted the “most extravagant claims” of big-data proponents very differently, namely by analyzing the theories on which machine learning is founded. They contrasted the assumptions underlying these theories, such as the law of large numbers, with the mathematical reality of complex biological systems. Specifically, they carefully identified genuine features of these systems, such as nonlinearities, nonlocality of effects, fractal aspects, and high dimensionality, and argued that they fundamentally violate some of the statistical assumptions implicitly underlying big-data analysis, like independence of events. They concluded that these discrepancies “may lead to false expectations and, at their nadir, even to dangerous social, economical and political manipulation.” To ameliorate the situation, the field of big-data analysis would need new strong theorems characterizing the validity of its methods and the numbers of data required for obtaining reliable insights. Succi and Coveney go as far as stating that too many data are just as bad as insufficient data [ 42 ].

While philosophical doubts regarding inductive methods will always persist, one cannot deny that -omics-based, high-throughput studies, combined with machine learning and big-data analysis, have been very successful [ 43 ]. Yes, induction cannot truly reveal general laws, no matter how large the datasets, but they do provide insights that are very different from what science had offered before and may at least suggest novel patterns, trends, or principles. As a case in point, if many transcriptomic studies indicate that a particular gene set is involved in certain classes of phenomena, there is probably some truth to the observation, even though it is not mathematically provable. Kepler’s laws of astronomy were arguably derived solely from inductive reasoning [ 34 ].

Notwithstanding the opposing views on inductive methods, successful strategies shape how we think about science. Thus, to take advantage of all experimental options while ensuring quality of research, we must not allow that “anything goes” but instead identify and characterize standard operating procedures and controls that render this emerging scientific method valid and reproducible. A laudable step in this direction was the wide acceptance of “minimum information about a microarray experiment” (MIAME) standards for microarray experiments [ 44 ].

A third dimension of the scientific method: Allochthonous reasoning

Parallel to the blossoming of molecular biology and the rapid rise in the power and availability of computing in the late 20th century, the use of mathematical and computational models became increasingly recognized as relevant and beneficial for understanding biological phenomena. Indeed, mathematical models eventually achieved cornerstone status in the new field of computational systems biology.

Mathematical modeling has been used as a tool of biological analysis for a long time [ 27 , 45 – 48 ]. Interesting for the discussion here is that the use of mathematical and computational modeling in biology follows a scientific approach that is distinctly different from the traditional and the data-driven methods, because it is distributed over two entirely separate domains of knowledge. One consists of the biological reality of DNA, elephants, and roses, whereas the other is the world of mathematics, which is governed by numbers, symbols, theorems, and abstract work protocols. Because the ways of thinking—and even the languages—are different in these two realms, I suggest calling this type of knowledge acquisition “allochthonous” (literally Greek: in or from a “piece of land different from where one is at home”; one could perhaps translate it into modern lingo as “outside one’s comfort zone”). De facto, most allochthonous reasoning in biology presently refers to mathematics and computing, but one might also consider, for instance, the application of methods from linguistics in the analysis of DNA sequences or proteins [ 49 ].

One could argue that biologists have employed “models” for a long time, for instance, in the form of “model organisms,” cell lines, or in vitro experiments, which more or less faithfully reflect features of the organisms of true interest but are easier to manipulate. However, this type of biological model use is rather different from allochthonous reasoning, as it does not leave the realm of biology and uses the same language and often similar methodologies.

A brief discussion of three experiences from our lab may illustrate the benefits of allochthonous reasoning. (1) In a case study of renal cell carcinoma, a dynamic model was able to explain an observed yet nonintuitive metabolic profile in terms of the enzymatic reaction steps that had been altered during the disease [ 50 ]. (2) A transcriptome analysis had identified several genes as displaying significantly different expression patterns during malaria infection in comparison to the state of health. Considered by themselves and focusing solely on genes coding for specific enzymes of purine metabolism, the findings showed patterns that did not make sense. However, integrating the changes in a dynamic model revealed that purine metabolism globally shifted, in response to malaria, from guanine compounds to adenine, inosine, and hypoxanthine [ 51 ]. (3) Data capturing the dynamics of malaria parasites suggested growth rates that were biologically impossible. Speculation regarding possible explanations led to the hypothesis that many parasite-harboring red blood cells might “hide” from circulation and therewith from detection in the blood stream. While experimental testing of the feasibility of the hypothesis would have been expensive, a dynamic model confirmed that such a concealment mechanism could indeed quantitatively explain the apparently very high growth rates [ 52 ]. In all three cases, the insights gained inductively from computational modeling would have been difficult to obtain purely with experimental laboratory methods. Purely deductive allochthonous reasoning is the ultimate goal of the search for design and operating principles [ 53 – 55 ], which strives to explain why certain structures or functions are employed by nature time and again. An example is a linear metabolic pathway, in which feedback inhibition is essentially always exerted on the first step [ 56 , 57 ]. This generality allows the deduction that a so far unstudied linear pathway is most likely (or even certain to be) inhibited at the first step. Not strictly deductive—but rather abductive—was a study in our lab in which we analyzed time series data with a mathematical model that allowed us to infer the most likely regulatory structure of a metabolic pathway [ 58 , 59 ].

A typical allochthonous investigation begins in the realm of biology with the formulation of a hypothesis ( Fig 3 ). Instead of testing this hypothesis with laboratory experiments, the system encompassing the hypothesis is moved into the realm of mathematics. This move requires two sets of ingredients. One set consists of the simplification and abstraction of the biological system: Any distracting details that seem unrelated to the hypothesis and its context are omitted or represented collectively with other details. This simplification step carries the greatest risk of the entire modeling approach, as omission of seemingly negligible but, in truth, important details can easily lead to wrong results. The second set of ingredients consists of correspondence rules that translate every biological component or process into the language of mathematics [ 60 , 61 ].

This mathematical and computational approach is distributed over two realms, which are connected by correspondence rules.

Once the system is translated, it has become an entirely mathematical construct that can be analyzed purely with mathematical and computational means. The results of this analysis are also strictly mathematical. They typically consist of values of variables, magnitudes of processes, sensitivity patterns, signs of eigenvalues, or qualitative features like the onset of oscillations or the potential for limit cycles. Correspondence rules are used again to move these results back into the realm of biology. As an example, the mathematical result that “two eigenvalues have positive real parts” does not make much sense to many biologists, whereas the interpretation that “the system is not stable at the steady state in question” is readily explained. New biological insights may lead to new hypotheses, which are tested either by experiments or by returning once more to the realm of mathematics. The model design, diagnosis, refinements, and validation consist of several phases, which have been discussed widely in the biomathematical literature. Importantly, each iteration of a typical modeling analysis consists of a move from the biological to the mathematical realm and back.

The reasoning within the realm of mathematics is often deductive, in the form of an Aristotelian syllogism, such as the well-known “All men are mortal; Socrates is a man; therefore, Socrates is mortal.” However, the reasoning may also be inductive, as it is the case with large-scale Monte-Carlo simulations that generate arbitrarily many “observations,” although they cannot reveal universal principles or theorems. An example is a simulation randomly drawing numbers in an attempt to show that every real number has an inverse. The simulation will always attest to this hypothesis but fail to discover the truth because it will never randomly draw 0. Generically, computational models may be considered sets of hypotheses, formulated as equations or as algorithms that reflect our perception of a complex system [ 27 ].

Impact of the multidimensional scientific method on learning

Almost all we know in biology has come from observation, experimentation, and interpretation. The traditional scientific method not only offered clear guidance for this knowledge gathering, but it also fundamentally shaped the way we think about the exploration of nature. When presented with a new research question, scientists were trained to think immediately in terms of hypotheses and alternatives, pondering the best feasible ways of testing them, and designing in their minds strong controls that would limit the effects of known or unknown confounders. Shaped by the rigidity of this ever-repeating process, our thinking became trained to move forward one well-planned step at a time. This modus operandi was rigid and exact. It also minimized the erroneous pursuit of long speculative lines of thought, because every step required testing before a new hypothesis was formed. While effective, the process was also very slow and driven by ingenuity—as well as bias—on the scientist’s part. This bias was sometimes a hindrance to necessary paradigm shifts [ 22 ].

High-throughput data generation, big-data analysis, and mathematical-computational modeling changed all that within a few decades. In particular, the acceptance of inductive principles and of the allochthonous use of nonbiological strategies to answer biological questions created an unprecedented mix of successes and chaos. To the horror of traditionalists, the importance of hypotheses became minimized, and the suggestion spread that the data would speak for themselves [ 36 ]. Importantly, within this fog of “anything goes,” the fundamental question arose how to determine whether an experiment was valid.

Because agreed-upon operating procedures affect research progress and interpretation, thinking, teaching, and sharing of results, this question requires a deconvolution of scientific strategies. Here I proffer that the single scientific method of the past should be expanded toward a vector space of scientific methods, with spanning vectors that correspond to different dimensions of the scientific method ( Fig 4 ).

The traditional hypothesis-based deductive scientific method is expanded into a 3D space that allows for synergistic blends of methods that include data-mining–inspired, inductive knowledge acquisition, and mathematical model-based, allochthonous reasoning.

Obviously, all three dimensions have their advantages and drawbacks. The traditional, hypothesis-driven deductive method is philosophically “clean,” except that it is confounded by preconceptions and assumptions. The data-mining–inspired inductive method cannot offer universal truths but helps us explore very large spaces of factors that contribute to a phenomenon. Allochthonous, model-based reasoning can be performed mentally, with paper and pencil, through rigorous analysis, or with a host of computational methods that are precise and disprovable [ 27 ]. At the same time, they are incomparable faster, cheaper, and much more comprehensive than experiments in molecular biology. This reduction in cost and time, and the increase in coverage, may eventually have far-reaching consequences, as we can already fathom from much of modern physics.

Due to its long history, the traditional dimension of the scientific method is supported by clear and very strong standard operating procedures. Similarly, strong procedures need to be developed for the other two dimensions. The MIAME rules for microarray analysis provide an excellent example [ 44 ]. On the mathematical modeling front, no such rules are generally accepted yet, but trends toward them seem to emerge at the horizon. For instance, it seems to be becoming common practice to include sensitivity analyses in typical modeling studies and to assess the identifiability or sloppiness of ensembles of parameter combinations that fit a given dataset well [ 62 , 63 ].

From a philosophical point of view, it seems unlikely that objections against inductive reasoning will disappear. However, instead of pitting hypothesis-based deductive reasoning against inductivism, it seems more beneficial to determine how the different methods can be synergistically blended ( cf . [ 18 , 27 , 34 , 42 ]) as linear combinations of the three vectors of knowledge acquisition ( Fig 4 ). It is at this point unclear to what degree the identified three dimensions are truly independent of each other, whether additional dimensions should be added [ 24 ], or whether the different versions could be amalgamated into a single scientific method [ 18 ], especially if it is loosely defined as a form of critical thinking [ 8 ]. Nobel Laureate Percy Bridgman even concluded that “science is what scientists do, and there are as many scientific methods as there are individual scientists” [ 8 , 64 ].

Combinations of the three spanning vectors of the scientific method have been emerging for some time. Many biologists already use inductive high-throughput methods to develop specific hypotheses that are subsequently tested with deductive or further inductive methods [ 34 , 65 ]. In terms of including mathematical modeling, physics and geology have been leading the way for a long time, often by beginning an investigation in theory, before any actual experiment is performed. It will benefit biology to look into this strategy and to develop best practices of allochthonous reasoning.

The blending of methods may take quite different shapes. Early on, Ideker and colleagues [ 65 ] proposed an integrated experimental approach for pathway analysis that offered a glimpse of new experimental strategies within the space of scientific methods. In a similar vein, Covert and colleagues [ 66 ] included computational methods into such an integrated approach. Additional examples of blended analyses in systems biology can be seen in other works, such as [ 43 , 67 – 73 ]. Generically, it is often beneficial to start with big data, determine patterns in associations and correlations, then switch to the mathematical realm in order to filter out spurious correlations in a high-throughput fashion. If this procedure is executed in an iterative manner, the “surviving” associations have an increased level of confidence and are good candidates for further experimental or computational testing (personal communication from S. Chandrasekaran).

If each component of a blended scientific method follows strict, commonly agreed guidelines, “linear combinations” within the 3D space can also be checked objectively, per deconvolution. In addition, guidelines for synergistic blends of component procedures should be developed. If we carefully monitor such blends, time will presumably indicate which method is best for which task and how the different approaches optimally inform each other. For instance, it will be interesting to study whether there is an optimal sequence of experiments along the three axes for a particular class of tasks. Big-data analysis together with inductive reasoning might be optimal for creating initial hypotheses and possibly refuting wrong speculations (“we had thought this gene would be involved, but apparently it isn’t”). If the logic of an emerging hypotheses can be tested with mathematical and computational tools, it will almost certainly be faster and cheaper than an immediate launch into wet-lab experimentation. It is also likely that mathematical reasoning will be able to refute some apparently feasible hypothesis and suggest amendments. Ultimately, the “surviving” hypotheses must still be tested for validity through conventional experiments. Deconvolving current practices and optimizing the combination of methods within the 3D or higher-dimensional space of scientific methods will likely result in better planning of experiments and in synergistic blends of approaches that have the potential capacity of addressing some of the grand challenges in biology.

Acknowledgments

The author is very grateful to Dr. Sriram Chandrasekaran and Ms. Carla Kumbale for superb suggestions and invaluable feedback.

Funding Statement

This work was supported in part by grants from the National Science Foundation ( https://www.nsf.gov/div/index.jsp?div=MCB ) grant NSF-MCB-1517588 (PI: EOV), NSF-MCB-1615373 (PI: Diana Downs) and the National Institute of Environmental Health Sciences ( https://www.niehs.nih.gov/ ) grant NIH-2P30ES019776-05 (PI: Carmen Marsit). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Expository Essays

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The Modes of Discourse—Exposition, Description, Narration, Argumentation (EDNA)—are common paper assignments you may encounter in your writing classes. Although these genres have been criticized by some composition scholars, the Purdue OWL recognizes the wide spread use of these approaches and students’ need to understand and produce them.

What is an expository essay?

The expository essay is a genre of essay that requires the student to investigate an idea, evaluate evidence, expound on the idea, and set forth an argument concerning that idea in a clear and concise manner. This can be accomplished through comparison and contrast, definition, example, the analysis of cause and effect, etc.

Please note : This genre is commonly assigned as a tool for classroom evaluation and is often found in various exam formats.

The structure of the expository essay is held together by the following.

• A clear, concise, and defined thesis statement that occurs in the first paragraph of the essay.

It is essential that this thesis statement be appropriately narrowed to follow the guidelines set forth in the assignment. If the student does not master this portion of the essay, it will be quite difficult to compose an effective or persuasive essay.

• Clear and logical transitions between the introduction, body, and conclusion.

Transitions are the mortar that holds the foundation of the essay together. Without logical progression of thought, the reader is unable to follow the essay’s argument, and the structure will collapse.

• Body paragraphs that include evidential support.

Each paragraph should be limited to the exposition of one general idea. This will allow for clarity and direction throughout the essay. What is more, such conciseness creates an ease of readability for one’s audience. It is important to note that each paragraph in the body of the essay must have some logical connection to the thesis statement in the opening paragraph.

• Evidential support (whether factual, logical, statistical, or anecdotal).

Often times, students are required to write expository essays with little or no preparation; therefore, such essays do not typically allow for a great deal of statistical or factual evidence.

• A bit of creativity!

Though creativity and artfulness are not always associated with essay writing, it is an art form nonetheless. Try not to get stuck on the formulaic nature of expository writing at the expense of writing something interesting. Remember, though you may not be crafting the next great novel, you are attempting to leave a lasting impression on the people evaluating your essay.

• A conclusion that does not simply restate the thesis, but readdresses it in light of the evidence provided.

It is at this point of the essay that students will inevitably begin to struggle. This is the portion of the essay that will leave the most immediate impression on the mind of the reader. Therefore, it must be effective and logical. Do not introduce any new information into the conclusion; rather, synthesize and come to a conclusion concerning the information presented in the body of the essay.

A complete argument

Perhaps it is helpful to think of an essay in terms of a conversation or debate with a classmate. If I were to discuss the cause of the Great Depression and its current effect on those who lived through the tumultuous time, there would be a beginning, middle, and end to the conversation. In fact, if I were to end the exposition in the middle of my second point, questions would arise concerning the current effects on those who lived through the Depression. Therefore, the expository essay must be complete, and logically so, leaving no doubt as to its intent or argument.

The five-paragraph Essay

A common method for writing an expository essay is the five-paragraph approach. This is, however, by no means the only formula for writing such essays. If it sounds straightforward, that is because it is; in fact, the method consists of:

• an introductory paragraph
• three evidentiary body paragraphs
• a conclusion

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Biology library

Course: biology library   >   unit 1, the scientific method.

• Controlled experiments
• The scientific method and experimental design

Introduction

• Make an observation.
• Ask a question.
• Form a hypothesis , or testable explanation.
• Make a prediction based on the hypothesis.
• Test the prediction.
• Iterate: use the results to make new hypotheses or predictions.

Scientific method example: Failure to toast

1. make an observation..

• Observation: the toaster won't toast.

2. Ask a question.

• Question: Why won't my toaster toast?

3. Propose a hypothesis.

• Hypothesis: Maybe the outlet is broken.

4. Make predictions.

• Prediction: If I plug the toaster into a different outlet, then it will toast the bread.

5. Test the predictions.

• Test of prediction: Plug the toaster into a different outlet and try again.
• If the toaster does toast, then the hypothesis is supported—likely correct.
• If the toaster doesn't toast, then the hypothesis is not supported—likely wrong.

6. Iterate.

• Iteration time!
• If the hypothesis was supported, we might do additional tests to confirm it, or revise it to be more specific. For instance, we might investigate why the outlet is broken.
• If the hypothesis was not supported, we would come up with a new hypothesis. For instance, the next hypothesis might be that there's a broken wire in the toaster.

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Home — Essay Samples — Science — Experiment — The Importance of the Scientific Method

The Importance of The Scientific Method

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Expository Writing: Definition and Examples

Hannah Yang

Table of Contents

What is expository writing, what is an expository paragraph, expository writing examples, how prowritingaid can help you with expository composition.

One of the most common types of writing is expository writing. Whether you’re a student taking an English class or a professional trying to communicate to others in your field, you’ll need to use expository writing in your day-to-day work.

So, what exactly does this term mean?

The short answer is that expository writing refers to any writing designed primarily to explain or instruct.

Read on to learn the definition of expository writing as well as some examples of what this type of writing can look like.

Before we look at examples of expository writing, let’s start with a quick definition of what this term actually means.

Expository Writing Definition

The term expository writing refers to any writing that’s designed to explain something. We use the word expository to describe any passage of writing that’s supposed to present information and help you understand it in an objective way.

Some common examples of expository writing include academic essays, textbooks, instructional guides, and news reports. Good expository writing should be factual, objective, and clear.

To better understand what this term means, think about the difference between a scientific article, a short story, and an advertisement.

The scientific article is considered expository writing because its primary purpose is to explain a particular topic in more detail. It presents data, analyzes what that data means, and focuses on the facts.  

On the other hand, the short story isn’t considered expository writing, because its core purpose isn’t to explain or inform—instead, it’s probably trying to entertain you or to take you on a journey. Short stories are narrative writing.

Similarly, an advertisement isn’t expository writing because its core purpose isn’t to explain or inform—instead, it’s trying to persuade you to buy what it’s selling. Advertisements are persuasive writing.   

Here’s a quick rundown of what expository essays should and shouldn’t do.

An expository essay should:

Teach the reader about a particular topic

Focus on the facts

Follow a clearly organized structure

Present information and details from credible sources

An expository essay should not:

Try to change the reader’s mind about something

Present the author’s personal opinions

Include made-up narratives or stories

Follow experimental or nonlinear structures

An expository paragraph is exactly what it sounds like—a paragraph of expository writing.

A well-written expository paragraph should follow a specific format to make it as clear and easy to read as possible. Most expository paragraphs do the following things:

Start with a topic sentence, which explains what the paragraph will be about

Then, include 3 – 5 body sentences that provide supporting details for the topic sentence

Finally, wrap things up with a closing sentence that summarizes what the paragraph has said

Writing an expository paragraph is a great way to practice expository writing. That’s because the paragraph follows the same structure as a more complex expository essay, just on a smaller scale.

Most expository essays should follow this format:  

Start with an introductory paragraph that includes the thesis statement, which tells the reader the core statement of the essay

Then, include 3 – 5 body paragraphs that provide factual evidence to support the thesis statement

Finally, wrap things up with a concluding paragraph that summarizes what the body paragraphs and thesis statement said

You can see the similarities between the two formats. If you can write a fantastic expository paragraph, you’ll be well-prepared to move on to writing a full expository essay.

Example of Expository Paragraph

Here’s an example of an expository paragraph that follows the structure described above.

The leading cause of death in the United States is heart disease, which can be fatal if it leads to heart attack or cardiac arrest. Heart attacks occur when a blockage in the coronary artery prevents oxygenated blood from reaching the heart. Cardiac arrests occur when the heart stops pumping entirely, which prevents the patient from breathing normally. Both of these problems can be deadly, even in seemingly healthy people who don’t have noticeable risk factors. As a result, heart disease is an important problem that many doctors and scientists are researching.

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There are many ways you can present information in an expository essay. Here are four of the most popular ways, along with examples of each one.  

Problem and Solution Essay

A problem and solution essay presents the reader with a problem and then considers possible solutions to that problem. 

Here’s an example passage you might find in a problem and solution essay:

Among the many proposed solutions to rising carbon emissions, one promising possibility is carbon trapping. Scientists are figuring out how to pull carbon emissions out of the atmosphere and trap it in less harmful forms, such as by injecting carbon dioxide underground so it will turn to stone.

Compare and Contrast Essay

This type of essay takes two subjects and compares and contrasts them. It focuses on highlighting the differences and similarities between those two things.

Here’s an example passage of this type of expository writing:

Though country music and R&B music have very different sounds, they also share many similarities. For one thing, both types of music embody a specific cultural identity. For another, both genres trace their roots back to the 1920s, when the Victor Talking Machine Company signed singers from the American South.

Classification Essay

In a classification essay, you describe the categories within a certain group of things.  

Here’s an example passage you might find in a classification essay:

There are three ways in which artificial intelligence might become stronger than humans in the future: high speed, high collective intelligence, and high quality. A speed AI would be able to perform calculations and experience the world much faster than humans. A collective intelligence, like a hive mind, would be able to break down a complex task into several parts and pursue them simultaneously. Finally, a quality AI would simply be able to solve more complex problems than humans could.

Process Essay

In a process essay, you give the reader the steps for completing a specific process. This is similar to a how-to guide or an instruction manual.   

Here’s an example passage you might find in this type of expository writing:

Caramelize the chopped onions in a frying pan. When the onions have caramelized, mix in the bell peppers, mushrooms, and tomatoes and stir for 4 – 6 minutes or until all the ingredients have softened. If you want to add meat, you can add ground beef and cook for another 4 – 6 minutes. Season with salt and pepper to taste.  

Good expository writing should be easy to read. After all, the purpose of exposition is to explain things to your readers, and you won’t be able to accomplish that if they have trouble understanding your writing.

That’s why ProWritingAid can help you write an expository essay. The grammar checker can help you ensure your sentences flow well, you’re not missing any necessary punctuation, and all your words are precise and clear.

Good luck, and happy writing!

Hannah is a speculative fiction writer who loves all things strange and surreal. She holds a BA from Yale University and lives in Colorado. When she’s not busy writing, you can find her painting watercolors, playing her ukulele, or hiking in the Rockies. Follow her work on hannahyang.com or on Twitter at @hannahxyang.

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4.1: Expository Essays

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Learning Objectives

• Understand the function and use of expository essays
• Identify eight types of expository essays
• Apply expository essay structure

What Is an Expository Essay?

An essay that explains a writer’s ideas by defining, explaining, informing, or elaborating on points to allow the reader to clearly understand the concept.

Many of your future academic workplace writing assignments will be expository–explaining your ideas or the significance of a concept or action. An expository essay allows the writer the opportunity to explain his or her ideas about a topic and to provide clarity for the reader by using:

• Explanations
• Definitions

It may also include the writer outlining steps of a procedure in a way that is straightforward for the reader to follow. It is purely informative and often contains elements of summary.

Imagine you need to verbally explain a concept to your classmates, maybe a behavioural theory. What are the key elements on which you would focus? How would you organize the information? You could explain who came up with the theory, the specific area of study to which it is related, its purpose, and the significant details to explain the theory. Telling these four elements to your classmates would give them a complete, yet summarized, picture of the theory, so they could apply the theory in future discussions.

Although you did this verbally, you were still fulfilling the elements of an expository essay by providing definition, details, explanations, and maybe even facts if you have a really good memory. This is the same process that you would use when you write an expository essay. You may actually be doing this all the time; for example, when you are giving someone directions to a place or explaining how to cook something. In the following sections of the chapter, you will practise doing this more in different expository written forms.

The Structure of an Expository Essay

Sections versus paragraphs.

Before looking at the general structure of an expository essay, you first need to know that in your post-secondary education, you should not consider your essay as writing being constructed with five paragraphs as you might have been used to in high school. You should instead think of your essay in terms of sections (there may be five), and each section may have multiple paragraphs.

To understand further why you need to think beyond the five-paragraph essay, imagine you have been asked to submit a six-page paper (approximately 1,500 words). You already know that each paragraph should be roughly 75 to 200 words long. If you divide the required word count by five paragraphs (1,500 by 5), you end with 300 words per paragraph, way above the number you should have in a paragraph. If your paragraphs are too long, they likely have too many ideas and your reader may become confused. Your paragraphs should be two-third of a page at most, and never longer than a page.

Instead, if you think of your essays being divided into sections (with possibly more than one paragraph per section), your writing will likely be more organized and allow your reader to follow your presentation of ideas without creating too much distance between your paragraph’s supporting points and its topic sentence.

As you will see in Section 4.5, some essay forms may require even more than five paragraphs or sections because of how many points are necessary to address. For the rest of this chapter, the term paragraph will also imply section.

Sections of an Expository Essay

An expository essay, regardless of its purpose, should have at least five sections, which are:

• Introduction
• First body section/paragraph
• Second body section/paragraph
• Third body section/paragraph
• Conclusion.

The introduction should state the topic of your paper: your thesis statement as well as brief signposts of what information the rest of the paper will include. That is, you only want to mention the content of the body paragraphs; you do not want to go in to a lot of detail and repeat what will be in the rest of the essay.

The first body section or paragraph should focus on one of your main points and provide evidence to support that point. There should be two to three supporting points: reasons, facts, statistics, quotations, examples, or a mix of these. Both the second and third body sections should follow the same pattern. Providing three body sections with one point each that supports the thesis should provide the reader with enough detail to be convinced of your argument or fully understand the concept you are explaining. However, remember that some sections will require more explanation, and you may need to separate this information into multiple paragraphs.

You can order your sections in the most logical way to explain your ideas. For example, if you are describing a process, you may use chronological order to show the definite time order in which the steps need to happen. You will learn about the different ways to organize your body paragraphs in the next chapter.

The concluding paragraph , or conclusion, can be a little tricky to compose because you need to make sure you give a concise summary of the body paragraphs, but you must be careful not to simply repeat what you have already written. Look back at the main idea of each section/paragraph, and try to summarize the point using words different from those you have already used. Do not include any new points in your concluding paragraph.

Consider Your Audience: How Much Do They Know?

Later in this chapter, you will work on determining and adapting to your audience when writing, but with an expository essay, since you are defining or informing your audience on a certain topic, you need to evaluate how much your audience knows about that topic (aside from having general common knowledge). You want to make sure you are giving thorough, comprehensive, and clear explanations on the topic. Never assume the reader knows everything about your topic (even if it is covered in the reader’s field of study). For example, even though some of your instructors may teach criminology, they may have specialized in different areas from the one about which you are writing; they most likely have a strong understanding of the concepts but may not recall all the small details on the topic. If your instructor specialized in crime mapping and data analysis for example, he or she may not have a strong recollection of specific criminological theories related to other areas of study. Providing enough background information without being too detailed is a fine balance, but you always want to ensure you have no gaps in the information, so your reader will not have to guess your intention. Again, we will practise this more in Section 4.9.

What Comes Next?

In the next eight sections (4.2 through 4.9), we will look at different expository modes, or rhetorical modes, you will often be assigned. These are:

• Illustration
• Description
• Classification
• Process analysis
• Compare and contrast
• Cause and effect

Rhetorical modes refers simply to the ways to communicate effectively through language. As you read about these modes, keep in mind that the rhetorical mode a writer chooses depends on his or her purpose for writing. Sometimes writers incorporate a variety of modes in any one essay. In this chapter, we also emphasize the rhetorical modes as a set of tools that will allow you greater flexibility and effectiveness in communicating with your audience and expressing your ideas.

In a few weeks, you will need to submit your first essay–an expository sample–and you will be given the choice of topic: one from each of the modes. Think about which types of expository essays are easier and which are more challenging for you. As mentioned, as you progress through your studies, you will be exposed to each of these types. You may want to explore a mode you find more challenging than the others in order to ensure you have a full grasp on developing each type. However, it is up to you. As you work through the sections, think about possible topics you may like to cover in your expository essay and start brainstorming as you work through the self-practice exercises.

After we explore each of the individual modes in the eight sections that follow, we will look at outlining and drafting; it is at this point you will want to fine tune and narrow the topic you will write about, so you can focus on that when doing the exercises.

How to Write an Expository Essay: Definition, Outline, Writing Tips, and Examples

In the realm of academic writing, this type of essay stands as a beacon of clarity, demanding writers to illuminate a subject with precision and objectivity. Whether you're a seasoned essayist or a student embarking on your first exploration of this genre, mastering the art of expository writing is a valuable skill that transcends disciplines. This form of essay invites you to delve into expository essay topics, dissect their intricacies, and present your findings in a straightforward manner. 

In this comprehensive guide, we will explore the terrain of expository writing, unraveling the techniques and strategies that transform a mere composition into a beacon of insight. From understanding the fundamental principles to honing your ability to craft a compelling thesis, join us on a journey that promises to demystify the process of writing, empowering you to articulate ideas with clarity and purpose. Or, you can get our essay writing help and take care of other important tasks set for today.

What Is an Expository Essay

An expository essay is a form of academic writing that aims to elucidate, clarify, and present a balanced analysis of a particular topic or idea. Unlike other essay types that may delve into personal opinions or narratives, the expository essay emphasizes objectivity and factual accuracy. The primary objective is to provide a clear and comprehensive explanation of the chosen subject, exploring its various facets, presenting evidence, and ensuring a logical progression of ideas. 

According to an expository essay definition, this genre requires the writer to delve into research, organize information systematically, and deliver a coherent and informative piece that educates the reader on the chosen topic. Whether investigating a scientific concept, historical event, or literary work, it serves as a vehicle for conveying knowledge in a concise, lucid manner.

Expository Essay Examples

An expository essay example serves as a valuable tool for students, offering a concrete illustration of the structure, style, and depth expected in this genre of writing. By studying examples, students gain insights into effective thesis formulation, organizing ideas within paragraphs, and integrating supporting evidence to bolster arguments. 

Additionally, examples showcase how to balance factual accuracy and engaging prose, providing a model for clear and concise communication. Students can draw inspiration from the content and presentation of well-crafted expository essays, honing their own skills in research, analysis, and effective expression. By the way, we have an interesting autobiography example , so check it out!

Example 1: “The Evolution of Artificial Intelligence”

This expository essay explores the multifaceted evolution of artificial intelligence (AI), examining its historical roots, contemporary applications across various industries, and the consequential societal impact. It provides a comprehensive overview of AI's journey from philosophical debates and early computational developments to its current role as a transformative force in healthcare, finance, manufacturing, and entertainment. Additionally, the essay addresses ethical considerations surrounding the widespread adoption of AI, including concerns related to job displacement, privacy, and responsible development. Ultimately, it navigates the complex landscape of artificial intelligence, shedding light on its remarkable advancements and its challenges to our ever-changing society.

Example 2: “The Benefits of Outdoor Education for Children”

This essay highlights the advantages of outdoor education for children, emphasizing its positive impacts on their physical, mental, and social development. It argues that outdoor activities like hiking, camping, and team sports not only promote physical health by encouraging movement and reducing sedentary behavior but also contribute to mental well-being by providing a respite from everyday stressors and fostering a connection with nature. Furthermore, it suggests that exposure to outdoor environments cultivates environmental awareness and a sense of stewardship among children.

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Types of Expository Essay

Expository essays come in several distinct types, each serving a unique purpose and requiring specific approaches to convey information effectively. One common categorization includes:

• Descriptive Expository Essay. This type focuses on painting a vivid picture of a subject, using sensory details to engage the reader's imagination. It aims to create a clear and sensory-rich portrayal of a person, place, object, or experience.
• Process Expository Essay. Here, the writer breaks down a complex process or procedure into manageable steps, providing a detailed and sequential explanation. This type of essay is instructional, guiding readers through a series of actions to achieve a specific outcome.
• Comparison and Contrast Expository Essay. This form involves analyzing similarities and differences between two or more subjects, offering insights into their shared characteristics or divergent qualities. It requires a careful examination of the chosen elements to highlight their relationships.
• Cause and Effect Expository Essay. Focused on exploring the reasons behind an occurrence and its subsequent consequences, this type delves into the cause-and-effect relationships within a given topic. Writers elucidate the connections between actions and outcomes, fostering a deeper understanding of the subject matter.
• Problem and Solution Expository Essay. Addressing real-world issues, this essay type identifies a specific problem, analyzes its root causes, and proposes viable solutions. It encourages critical thinking and problem-solving skills, compelling readers to consider alternative approaches to challenges.
• Definition Expository Essay. This essay seeks to clarify and explain the meaning of a particular term, concept, or idea. Writers provide a comprehensive definition, often including examples and illustrations to ensure readers grasp the essence of the subject.
• Cause and Effect Expository Essay. This type of essay examines the reasons behind a particular phenomenon or event and explores its subsequent effects. It aims to establish a clear cause-and-effect relationship, allowing readers to comprehend the interconnected elements of the topic.

Understanding these diverse types of essays empowers writers to choose the most suitable approach for effectively conveying information and achieving their communicative goals. Our experts can rewrite essay that you already did according to any of the above-mentioned types.

Expository Essay Topics

Selecting compelling expository essay topics requires thoughtful consideration of both personal interest and the potential engagement of the intended audience. Start by identifying subjects that genuinely captivate your curiosity or align with your expertise, as this enthusiasm will naturally infuse vigor into your writing. Additionally, assess the topic's relevance in the broader context, ensuring it addresses contemporary issues or timeless themes. 

Consider the audience's interests, aiming for subjects that resonate with their experiences or evoke a sense of shared relevance. Striking a balance between uniqueness and accessibility is key—opt for topics that allow you to offer fresh perspectives while ensuring there is ample research material available. Ultimately, the best topics seamlessly blend your passion, the audience's interests, and the broader significance of the chosen subject, ensuring a captivating and informative exploration for both writer and reader alike. Here are expository essay ideas from our writers for your inspiration:

• The influence of art on human emotions.
• Exploring the life cycle of a star.
• Tips for sustainable living in urban areas.
• The impact of social media on political awareness.
• How to cultivate a positive mindset in challenging times.
• The history and cultural significance of tattoos.
• The process of recycling electronic waste.
• Benefits of incorporating meditation into daily routines.
• The role of laughter in maintaining mental health.
• Understanding the psychology of decision-making.
• The impact of fashion on individual expression.
• Tips for effective conflict resolution in relationships.
• The science behind the sense of taste.
• The significance of biodiversity in ecosystems.
• Exploring the history of traditional folk music.
• How to foster a sense of community in a neighborhood.
• The benefits of learning a musical instrument.
• The evolution of communication technologies.
• The process of seed germination in plants.
• Tips for creating a productive home office space.
• The impact of artificial intelligence on job markets.
• Understanding the concept of emotional intelligence.
• The benefits of practicing gratitude daily.
• The history and cultural importance of tea.
• How to develop effective public speaking skills.
• Exploring the world of virtual reality technology.
• The significance of water purification methods.
• Tips for maintaining a healthy work-life balance.
• The process of making sustainable food choices.
• The role of literature in shaping societal norms.

Expository Essay Outline

An outline for expository essay is a structured plan that serves as a roadmap for organizing the main ideas and supporting details of the essay in a logical and coherent manner. While the specific structure may vary based on the assignment or preferences, a typical outline generally includes the following components, beginning with how to start an expository essay:

Expository Essay Introduction

• Hook or attention-grabbing statement.
• Background information on the topic.
• Clear thesis statement that presents the main idea.

Body Paragraphs (usually three or more)

• Topic sentence for each paragraph, presenting a main point or supporting idea.
• Supporting evidence, facts, or examples to illustrate and explain the topic sentence.
• Analysis or interpretation of the evidence to connect it back to the thesis.

Expository Essay Conclusion

• Restatement of the thesis in different words.
• Summary of the main points discussed in the body paragraphs.
• Concluding thoughts or insights, possibly suggesting implications or future considerations.

Transitions

• Smooth transitions between paragraphs to ensure a cohesive flow of ideas.
• Clear connections between sentences and paragraphs to guide the reader through the essay.

Revising and Editing

• Space for notes on areas that may need revision or improvement.
• Consideration of clarity, coherence, and overall effectiveness.

By creating an expository essay outline, a college essay writer can organize their thoughts, ensure a logical progression of ideas, and maintain a clear and concise structure. This framework helps writers stay focused on the main purpose of the essay – to inform, explain, or analyze a particular subject – while providing a roadmap for readers to follow and comprehend the information presented.

How to Write an Expository Essay Step by Step

Writing an expository essay involves a systematic process that ensures clarity, coherence, and effectiveness in conveying information. Here is a step-by-step guide to help you craft an expository essay:

Choose a Topic

• Select a topic that interests you and aligns with the purpose of an expository essay – to inform, explain, or analyze a subject.

Conduct Research

• Gather relevant and credible information to support your chosen topic. 
• Utilize reputable sources such as academic journals, books, and reliable websites.

Create an Outline

• Develop a clear and organized outline that includes the introduction, body paragraphs, and conclusion.
• Each section should have a specific purpose and contribute to the overall coherence of the essay.

Write the Introduction

• Start with an attention-grabbing hook that relates to your topic. 
• Provide background information and context, leading to a concise and focused thesis statement that outlines the main idea.

Develop Body Paragraphs

• Each body paragraph should begin with a clear topic sentence that introduces the main point. 
• Support the topic sentence with evidence, facts, or examples. 
• Ensure a logical flow between paragraphs, using transitions to guide the reader.

Provide Evidence

• Support your points with credible evidence and examples. 
• Ensure that each piece of evidence directly relates to the topic sentence and supports the overall thesis of the essay.

Analyze and Interpret

• After presenting evidence, analyze and interpret it. 
• Explain the significance of the evidence and how it relates to your thesis. 
• This step helps to ensure that your audience understands the relevance of the information presented.

Write the Conclusion

• Summarize the main points discussed in the body paragraphs without introducing new information. 
• Restate the thesis in different words and offer concluding insights or implications related to the topic.

Revise and Edit

• Review your essay for clarity, coherence, and consistency. 
• Check for grammatical errors and awkward phrasing, ensuring a smooth flow of ideas. 
• Consider feedback from others or take a break before revising to gain a fresh perspective.
• Carefully proofread your essay to catch any remaining errors, typos, or issues. 
• Pay attention to grammar, punctuation, and argumentative essay format .

By following these steps, you can systematically approach the writing process and create a well-organized and informative expository essay. Remember to stay focused on the purpose of informing, explaining, or analyzing the chosen topic throughout the entire writing process.

Final Thoughts

Learning how to write an expository essay offers students several important advantages. First off, it helps them express their thoughts clearly and organize ideas effectively, skills that are useful not only in academics but also in various professional situations where clear communication is key. 

Moreover, writing expository essays improves critical thinking as students practice analyzing information, connecting ideas, and presenting well-supported arguments. This skill is valuable in everyday decision-making and problem-solving scenarios. 

Additionally, the process of crafting such essays enhances research abilities, teaching students how to find, evaluate, and use information effectively. Overall, mastering expository writing equips students with practical, transferable skills that can positively impact their academic and professional pursuits. You can use our research paper service to cope with assignments better and faster.

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Expository Writing – Examples, Meaning And Types Of Expository Writing Style

Have you ever paid attention to the infinite scrolling feature on the internet? Whether it’s a Buzzfeed article or Twitter…

Have you ever paid attention to the infinite scrolling feature on the internet? Whether it’s a Buzzfeed article or Twitter updates, you can’t seem to stop scrolling, hungry for more news. If you’ve read a ‘how to do something’ article or a condensed version of a long news report, then you’ve encountered various examples of expository writing.

While expository writing dominates the content world today, businesses have been relying on this writing style for ages. Every professional should understand the dos and don’ts of expository writing for effective business communication and workplace success.

What Is Expository Writing?

The different types of expository writing, the importance of expository writing in business.

Before we explore the basic tenets of expository writing, let’s look at what exposition means. At its simplest, it means explaining something. The purpose of the expository writing style is to enlighten or instruct. In other words, it means to present an idea or relevant discussion that helps explain or analyze information. Some of the most common examples of expository writing include scientific reports, academic essays and magazine articles.

An expository writer can’t assume that potential readers have prior knowledge or understanding about the information that they present. It’s best to avoid beating around the bush and highlight things as they are. The main features of expository writing style include:

It needs to be informative and highlight relevant details for better understanding

There should be clarity and an expository writer should know what they’re talking about

Well-written expository pieces continue to focus on the main topic and list events in an organized manner

The use of the first-person narrative should be avoided; instead, second-person instruction is much more effective

It should steer clear of personal thoughts and opinions and present an unbiased version of the information

Most of us have written some form of expository writing whether it was in school, college or office. Here are the various types of expository writing that’ll help you deliver ideas clearly:

Problem And Solution

As the name suggests, you identify the problem, provide details about it to explain it and suggest a solution(s) to tackle it. You have to justify the solution with sufficient data and propose ways to implement those solutions.

Cause And Effect

It conveys why something happened and how will it impact something. The outcomes suggested can be either true or hypothetical but the author should validate them.

Compare And Contrast

In this type of expository writing, the writer compares the similarities and contrasts the differences between the two subjects. This is only possible when subjects belong to the same category. For example, a comparative study between indoor and outdoor stadiums.

Definition And Classification

It provides a complete description of a subject, elaborating on the meaning, types and examples. It includes terms that have concrete meaning (e.g., objects) as well as abstract meanings (e.g., emotions).

How-To/Process

This type of writing is instructive and tells the reader about a task and how to do it. The reader may also include step-by-step instruction for easier understanding. Cook-books and user manuals are ideal examples of expository writing.

Take any typical day at work and reflect on the kinds of tasks you’re involved with. Written communication will be a commonly recurring activity. Business communication is one of the cornerstones of professional success. It’s important that you become familiar with the meaning of expository writing and establish yourself as an effective communicator. People are bound to take notice at work. Here are some expository writing tips that you should consider.

Work with the information that you’re most familiar with. For example, if you don’t know how to begin your email, write the body and conclusion before the introduction. It’s easier to map your purpose, identify your thoughts and then put them into writing.

Case-studies and projects can’t be considered authentic unless you back your report with ample data. In order to be persuasive and convince your clients or customers, you need to provide them with substantial evidence.

Always come straight to the point as readers won’t always have the required attention span. It’s best to present your data succinctly and directly because the topics are likely to be dry or boring. However, steer clear from jargon and other technical words and make your writing accessible.

Always pay attention to the format. An effective way to organize your thoughts is to prewrite and outline. It’ll help you narrow down the topics or details you want to discuss. It’s best to have a concluding paragraph that reiterates your position.

Most of all, make sure that you edit and proofread your draft. You don’t want to give a wrong impression, affecting your professional relationship and reputation. Pay attention to detail and never leave room for confusion.

Harappa Education’s Writing Proficiently course will help you structure your thoughts, polish your writing style and teach you to write clearly, concisely and compellingly. The Pyramid Principle will guide you in presenting key points of messages upfront with supporting evidence. Make your business communication effective and leave lasting impressions with your expository writing style.

Explore topics such as Significance of  Writing Skills , Different Types of  Writing Styles ,  Descriptive Writing ,  Process of Writing  &  How to Write an Email  from Harappa Diaries and polish your writing skills.

expository essay

What is expository essay definition, usage, and literary examples, expository essay definition.

An  expository essay  [ik-SPOZ-ih-tohr-ee ess-ay] is an essay in which the writer researches a topic and uses evidence to inform their readers or clarify the topic. They can take many forms, including a how-to essay, an essay that defines something, or an essay that studies a problem and offers a solution.

The Five-Paragraph Model

Most expository essays follow the five-paragraph essay model:

• Introduction:  The introduction contains the thesis statement or main point of the essay. Here, the writer describes the subject and gives necessary  context .
• Body:  This section is usually three or more paragraphs and offers supporting evidence for the thesis.
• Conclusion:  The conclusion revisits the thesis and summarizes the writer’s main points.

Types of Expository Essays

There are several types of expository essays that can be written.

• Cause and Effect:  These essays question why something happened and the outcome of that occurrence. For example, an essay of this type might question why there’s a large homeless population in Seattle and what effects it has on the city and its citizens.
• Classification:  These break a broad subject down into several, in-depth subcategories. A classification essay might study the various kinds of movies, define genres, and break the most common genres down by subgenre (for example, action thriller and action adventure as subgenres of the action genre).
• Compare and Contrast:  These essays lay out the similarities and differences of at least two subjects. One such essay might compare two different novels by the same author. These essays can explore the pros and cons of different choices as well, like living in the city versus living in the country.
• Definition:  As indicated, a definition essay describes or defines something. For example, it might define the internet and provide a detailed explanation of how it works.
• How-To:  Also called a process essay, a how-to essay gives the reader steps for creating or doing something. For example, a process essay might walk its reader through setting a table, step by step.
• Problem and Solution:  This type of essay explores a problem and, using evidential support, offers potential solutions. For example, a writer might consider the example of Seattle’s homeless population, cite a solution that other cities have used successfully, and propose that same solution for Seattle.

Other Forms of Expository Writing

In addition to the aforementioned, there are other uses for expository writing. Most commonly:

• Newspaper articles
• Encyclopedic entries
• Manuals/assembly instructions

Expository vs. Argumentative Essays

Expository essays are like argumentative essays in that they both require research. Unlike argumentative essays, expository essays are meant to inform their audience rather than persuade it.

Argumentative essays are often controversial and contain the writer’s personal opinions, whereas expository essays give factual information and explore a topic from many  perspectives . Educational spheres often use expository essays to test writing ability, reading comprehension, and/or the writer’s understanding of a topic.

Examples of Expository Essays

1. Susan Sontag, “Notes on ‘Camp’”

This is a definition essay that explores the meaning and usage of the slang word  camp . When she wrote the essay in 1964, people used the word to describe a person or thing as exaggerated, effeminate, or theatrical. Sontag suggests that camp isn’t a solid concept but rather a sensibility, and she acknowledges its connection to contemporary gay culture. Her definition of camp is given in the following passage:

[Camp] is not a natural mode of sensibility, if there be any such. Indeed the essence of Camp is its love of the unnatural: of artifice and exaggeration. And Camp is esoteric–something of a private code, a badge of identity even, among small urban cliques.

2. David Foster Wallace, “Consider the Lobster”

Herein, Wallace reviews the 2003 Main Lobster Festival and questions the morality of boiling lobsters alive. He examines the problem from all facets, including whether a lobster feels pain, without directly asserting his opinion. After descriptions of the festival, physical properties of lobsters, and the common use of the crustaceans, Wallace poses the main question of the essay:

So then here is a question that’s all but unavoidable at the World’s Largest Lobster Cooker, and may arise in the kitchens across the U.S. Is it all right to boil a sentient creature alive just for our gustatory pleasure? A related set of concerns: Is the previous question irksomely PC or sentimental? What does “all right” even mean in this context? Is it all just a matter of individual choice?

3. Rebecca Solnit, “The Longest War”

From Solnit’s 2014 book of essays,  Men Explain Things to Me , “The Longest War” explores issues of male violence against women. Solnit uses both statistical and  anecdotal  evidence to inform her audience of the issue, which supports some of her argumentative essays that appear later in the book:

[T]hough a rape is reported only every 6.2 minutes in this country, the estimated total is perhaps five times as high. Which means that there may be very nearly a rape a minute in the United States. It all adds up to tens of millions of rape victims. A significant portion of the women you know are survivors.

Further Resources on Expository Essays

You can find more examples of expository essays on  LiteraryDevices.net .

Bibme.org  offers guidance for writing expository essays.

Essaytigers.com  provides step-by-step writing instructions and an additional argumentative essay and expository essay comparison.

Related Terms

• Argumentative Essay
• Expository Writing

Expository Essay

Types Of Expository Writing

Types of Expository Writing - Definition and Examples

11 min read

Published on: Aug 10, 2018

Last updated on: Nov 15, 2023

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Writing an expository essay is quite more difficult than any other type of essay. Creating an impressive essay requires time, thorough research, skills, and knowledge. 

There are 10 main types of expository writing, each of which has a unique objective. They all are similar in nature but serve a different purpose. 

Read on to learn what are the different types of expository writing and what purpose they serve.

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Expository Writing Definition

Expository writing is a genre of writing that is used to explain, describe, inform, or clarify a particular expository essay topic to the reader. 

Unlike other forms of writing that may involve personal opinions or persuasion, the characteristics of expository writing include a focus on providing factual information in a clear and organized manner.

Expository essay writing is a very common form of writing; journals, newspaper articles, and essays usually demonstrate this type of writing.

While writing an expository essay, you need to explain everything in detail to make the idea clear for the reader. You can take help from  expository essay examples  to see what elements make a perfect expository essay.

What are the Types of Expository Writing?

There are 10 types of expository essay writing, including:

• Compare and Contrast Essay 

Cause and Effect Essay

• Problem and Solution Essay 

Process Essay

Definition essay.

• Classification Essay 
• Descriptive Writing
• Exploratory Writing
• Anecdotal Evidence
• Sequential Writing

Let’s take a look at common types of expository writing one by one.

Compare and Contrast Essay

The  compare and contrast essay  is a type of essay in which the writer compares and contrasts two things. The writer compares the similarities between the two selected subjects and contrasts the differences in those subjects. The subjects should belong to the same category.

In the  cause and effect essay , the writer tries to find the cause of something; why did something happen? and what effects it might have. This type of essay has built around the reason that caused something to happen and its possible impacts.

There are two ways to structure a cause and effect essay:

• Block structure:  All the causes are presented first, and then all of their effects.
• Chain structure:  Each of the causes is followed by its effect straight away.

This essay could be based on assumptions or could be based on facts, but either way, they should be validated.

Problem and Solution Essay

In the  problem solution essay , the writer identifies a problem and then proposes its solution. The writer examines the particular subject from various aspects and perspectives prior to providing a solution. This essay is somewhat similar to the cause and effect essay.

The process essay refers to the process of something, i.e., how to make an apple pie. This type of writing includes a step-by-step process of making or doing something.

This is how you write a process essay; it provides the complete process of doing something. The goal is to provide the process in such a way that the reader can follow the sequence without any mistakes.

The  definition essay  is a type of expository essay that gives a complete description of the topic. It explains what the term or the topic of the essay exactly means. Some terms have concrete meanings like glass, book, etc. Whereas some have abstract meanings like love, respect, honor, etc.

The definition essay revolves around explaining the purpose, what, why, and how aspects of the topic of the essay. This essay could start with the dictionary definition and ultimately provide the extended definition.

Classification Essay

Classification essays are a type of expository writing that categorizes and organizes objects, people, ideas, or concepts into distinct groups based on shared characteristics, features, or criteria. The goal is to help readers better understand the relationships and differences between these categories. 

Descriptive Writing 

Descriptive writing is a type of expository writing that aims to paint a vivid picture of a person, place, object, event, or concept in the reader's mind. It uses sensory details and vivid language to create a sensory experience for the audience. 

Exploratory Writing 

Exploratory writing aims to investigate a topic or question from multiple angles, often without taking a definitive stance. It allows the writer and reader to explore various viewpoints and ideas. 

Anecdotal Evidence 

Anecdotal evidence refers to personal stories, individual accounts, or isolated examples that are used to support a claim or argument. While anecdotal evidence can be compelling and relatable, it is based on personal experiences and may not reflect broader trends or realities. 

Sequential Writing 

Sequential writing, also known as chronological writing, involves organising information or events in a clear, time-based order. This approach is often used when presenting a series of actions, or events in a logical sequence, making it easier for readers to understand a process.

Types of Expository Writing Examples

You can use these types of expository writing PDF examples as a guide when writing your own paper. These examples show you what types of information to include and how it all comes together in one cohesive piece.

Types of Expository Writing Middle School

Types of Expository Writing Structure

Expository Writing Examples Pdf

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Tips for Expository Writing

The following are some easy types of expository writing strategies and tips. They will help you write an amazing essay.

• Write your introduction in the most interesting way possible. Start with a hook, thesis statement, or exciting detail to make readers want more.
• Make your essay clear and concise so that it doesn't confuse readers.
• There are many ways to support your topic. You can use facts, data, and authentic sources.
• It is important to consider the audience of your paper before you start writing.
• Use authoritative sources to gather data for your paper.
• To avoid any errors in the essay, proofread and edit it before submitting it.

As we've explored various types of expository writing, it's clear that each type serves a unique purpose. 

By choosing the right type, you can engage readers and make complex ideas accessible to a wide audience.

Still, if you need help with an expository essay or any other type of essay, you can hire a professional essay writer. 

My PerfectWords.com  is an online writing service that you can rely on for getting quality essay help. 

Our  expository essay writing service has a team of experts that can create a perfect essay in no time. They can cater to all of your expository essay writing needs. From choosing a topic to crafting an expository essay outline to essay writing, they do it all.

So request " write my essay for me online ' and get your essay in no time!

Frequently Asked Questions

What are the 4 characteristics of expository text.

The four main characteristics of expository text are;

• Informative
• Organization of the text

What are the five elements of expository writing?

The main five elements of expository writing are;

• Topic sentence
• Organization
• Transitions
• Evidence and examples 

How many types of expository writing are there?

There are generally eight common types of expository writing:

• Process/How-To Writing
• Cause and Effect Writing
• Compare and Contrast Writing
• Problem-Solution Writing
• Definition Writing
• Classification Writing

Caleb S. (Literature, Marketing)

Caleb S. has been providing writing services for over five years and has a Masters degree from Oxford University. He is an expert in his craft and takes great pride in helping students achieve their academic goals. Caleb is a dedicated professional who always puts his clients first.

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