General Education
Think about something strange and unexplainable in your life. Maybe you get a headache right before it rains, or maybe you think your favorite sports team wins when you wear a certain color. If you wanted to see whether these are just coincidences or scientific fact, you would form a hypothesis, then create an experiment to see whether that hypothesis is true or not.
But what is a hypothesis, anyway? If you’re not sure about what a hypothesis is--or how to test for one!--you’re in the right place. This article will teach you everything you need to know about hypotheses, including:
So let’s get started!
Merriam Webster defines a hypothesis as “an assumption or concession made for the sake of argument.” In other words, a hypothesis is an educated guess . Scientists make a reasonable assumption--or a hypothesis--then design an experiment to test whether it’s true or not. Keep in mind that in science, a hypothesis should be testable. You have to be able to design an experiment that tests your hypothesis in order for it to be valid.
As you could assume from that statement, it’s easy to make a bad hypothesis. But when you’re holding an experiment, it’s even more important that your guesses be good...after all, you’re spending time (and maybe money!) to figure out more about your observation. That’s why we refer to a hypothesis as an educated guess--good hypotheses are based on existing data and research to make them as sound as possible.
Hypotheses are one part of what’s called the scientific method . Every (good) experiment or study is based in the scientific method. The scientific method gives order and structure to experiments and ensures that interference from scientists or outside influences does not skew the results. It’s important that you understand the concepts of the scientific method before holding your own experiment. Though it may vary among scientists, the scientific method is generally made up of six steps (in order):
You’ll notice that the hypothesis comes pretty early on when conducting an experiment. That’s because experiments work best when they’re trying to answer one specific question. And you can’t conduct an experiment until you know what you’re trying to prove!
After doing your research, you’re ready for another important step in forming your hypothesis: identifying variables. Variables are basically any factor that could influence the outcome of your experiment . Variables have to be measurable and related to the topic being studied.
There are two types of variables: independent variables and dependent variables. I ndependent variables remain constant . For example, age is an independent variable; it will stay the same, and researchers can look at different ages to see if it has an effect on the dependent variable.
Speaking of dependent variables... dependent variables are subject to the influence of the independent variable , meaning that they are not constant. Let’s say you want to test whether a person’s age affects how much sleep they need. In that case, the independent variable is age (like we mentioned above), and the dependent variable is how much sleep a person gets.
Variables will be crucial in writing your hypothesis. You need to be able to identify which variable is which, as both the independent and dependent variables will be written into your hypothesis. For instance, in a study about exercise, the independent variable might be the speed at which the respondents walk for thirty minutes, and the dependent variable would be their heart rate. In your study and in your hypothesis, you’re trying to understand the relationship between the two variables.
The best hypotheses start by asking the right questions . For instance, if you’ve observed that the grass is greener when it rains twice a week, you could ask what kind of grass it is, what elevation it’s at, and if the grass across the street responds to rain in the same way. Any of these questions could become the backbone of experiments to test why the grass gets greener when it rains fairly frequently.
As you’re asking more questions about your first observation, make sure you’re also making more observations . If it doesn’t rain for two weeks and the grass still looks green, that’s an important observation that could influence your hypothesis. You'll continue observing all throughout your experiment, but until the hypothesis is finalized, every observation should be noted.
Finally, you should consult secondary research before writing your hypothesis . Secondary research is comprised of results found and published by other people. You can usually find this information online or at your library. Additionally, m ake sure the research you find is credible and related to your topic. If you’re studying the correlation between rain and grass growth, it would help you to research rain patterns over the past twenty years for your county, published by a local agricultural association. You should also research the types of grass common in your area, the type of grass in your lawn, and whether anyone else has conducted experiments about your hypothesis. Also be sure you’re checking the quality of your research . Research done by a middle school student about what minerals can be found in rainwater would be less useful than an article published by a local university.
Once you’ve considered all of the factors above, you’re ready to start writing your hypothesis. Hypotheses usually take a certain form when they’re written out in a research report.
When you boil down your hypothesis statement, you are writing down your best guess and not the question at hand . This means that your statement should be written as if it is fact already, even though you are simply testing it.
The reason for this is that, after you have completed your study, you'll either accept or reject your if-then or your null hypothesis. All hypothesis testing examples should be measurable and able to be confirmed or denied. You cannot confirm a question, only a statement!
In fact, you come up with hypothesis examples all the time! For instance, when you guess on the outcome of a basketball game, you don’t say, “Will the Miami Heat beat the Boston Celtics?” but instead, “I think the Miami Heat will beat the Boston Celtics.” You state it as if it is already true, even if it turns out you’re wrong. You do the same thing when writing your hypothesis.
Additionally, keep in mind that hypotheses can range from very specific to very broad. These hypotheses can be specific, but if your hypothesis testing examples involve a broad range of causes and effects, your hypothesis can also be broad.
Now that you understand what goes into a hypothesis, it’s time to look more closely at the two most common types of hypothesis: the if-then hypothesis and the null hypothesis.
First of all, if-then hypotheses typically follow this formula:
If ____ happens, then ____ will happen.
The goal of this type of hypothesis is to test the causal relationship between the independent and dependent variable. It’s fairly simple, and each hypothesis can vary in how detailed it can be. We create if-then hypotheses all the time with our daily predictions. Here are some examples of hypotheses that use an if-then structure from daily life:
In each of these situations, you’re making a guess on how an independent variable (sleep, time, or studying) will affect a dependent variable (the amount of work you can do, making it to a party on time, or getting better grades).
You may still be asking, “What is an example of a hypothesis used in scientific research?” Take one of the hypothesis examples from a real-world study on whether using technology before bed affects children’s sleep patterns. The hypothesis read s:
“We hypothesized that increased hours of tablet- and phone-based screen time at bedtime would be inversely correlated with sleep quality and child attention.”
It might not look like it, but this is an if-then statement. The researchers basically said, “If children have more screen usage at bedtime, then their quality of sleep and attention will be worse.” The sleep quality and attention are the dependent variables and the screen usage is the independent variable. (Usually, the independent variable comes after the “if” and the dependent variable comes after the “then,” as it is the independent variable that affects the dependent variable.) This is an excellent example of how flexible hypothesis statements can be, as long as the general idea of “if-then” and the independent and dependent variables are present.
Your if-then hypothesis is not the only one needed to complete a successful experiment, however. You also need a null hypothesis to test it against. In its most basic form, the null hypothesis is the opposite of your if-then hypothesis . When you write your null hypothesis, you are writing a hypothesis that suggests that your guess is not true, and that the independent and dependent variables have no relationship .
One null hypothesis for the cell phone and sleep study from the last section might say:
“If children have more screen usage at bedtime, their quality of sleep and attention will not be worse.”
In this case, this is a null hypothesis because it’s asking the opposite of the original thesis!
Conversely, if your if-then hypothesis suggests that your two variables have no relationship, then your null hypothesis would suggest that there is one. So, pretend that there is a study that is asking the question, “Does the amount of followers on Instagram influence how long people spend on the app?” The independent variable is the amount of followers, and the dependent variable is the time spent. But if you, as the researcher, don’t think there is a relationship between the number of followers and time spent, you might write an if-then hypothesis that reads:
“If people have many followers on Instagram, they will not spend more time on the app than people who have less.”
In this case, the if-then suggests there isn’t a relationship between the variables. In that case, one of the null hypothesis examples might say:
“If people have many followers on Instagram, they will spend more time on the app than people who have less.”
You then test both the if-then and the null hypothesis to gauge if there is a relationship between the variables, and if so, how much of a relationship.
If you’re going to take the time to hold an experiment, whether in school or by yourself, you’re also going to want to take the time to make sure your hypothesis is a good one. The best hypotheses have four major elements in common: plausibility, defined concepts, observability, and general explanation.
At first glance, this quality of a hypothesis might seem obvious. When your hypothesis is plausible, that means it’s possible given what we know about science and general common sense. However, improbable hypotheses are more common than you might think.
Imagine you’re studying weight gain and television watching habits. If you hypothesize that people who watch more than twenty hours of television a week will gain two hundred pounds or more over the course of a year, this might be improbable (though it’s potentially possible). Consequently, c ommon sense can tell us the results of the study before the study even begins.
Improbable hypotheses generally go against science, as well. Take this hypothesis example:
“If a person smokes one cigarette a day, then they will have lungs just as healthy as the average person’s.”
This hypothesis is obviously untrue, as studies have shown again and again that cigarettes negatively affect lung health. You must be careful that your hypotheses do not reflect your own personal opinion more than they do scientifically-supported findings. This plausibility points to the necessity of research before the hypothesis is written to make sure that your hypothesis has not already been disproven.
The more advanced you are in your studies, the more likely that the terms you’re using in your hypothesis are specific to a limited set of knowledge. One of the hypothesis testing examples might include the readability of printed text in newspapers, where you might use words like “kerning” and “x-height.” Unless your readers have a background in graphic design, it’s likely that they won’t know what you mean by these terms. Thus, it’s important to either write what they mean in the hypothesis itself or in the report before the hypothesis.
Here’s what we mean. Which of the following sentences makes more sense to the common person?
If the kerning is greater than average, more words will be read per minute.
If the space between letters is greater than average, more words will be read per minute.
For people reading your report that are not experts in typography, simply adding a few more words will be helpful in clarifying exactly what the experiment is all about. It’s always a good idea to make your research and findings as accessible as possible.
Good hypotheses ensure that you can observe the results.
In order to measure the truth or falsity of your hypothesis, you must be able to see your variables and the way they interact. For instance, if your hypothesis is that the flight patterns of satellites affect the strength of certain television signals, yet you don’t have a telescope to view the satellites or a television to monitor the signal strength, you cannot properly observe your hypothesis and thus cannot continue your study.
Some variables may seem easy to observe, but if you do not have a system of measurement in place, you cannot observe your hypothesis properly. Here’s an example: if you’re experimenting on the effect of healthy food on overall happiness, but you don’t have a way to monitor and measure what “overall happiness” means, your results will not reflect the truth. Monitoring how often someone smiles for a whole day is not reasonably observable, but having the participants state how happy they feel on a scale of one to ten is more observable.
In writing your hypothesis, always keep in mind how you'll execute the experiment.
Perhaps you’d like to study what color your best friend wears the most often by observing and documenting the colors she wears each day of the week. This might be fun information for her and you to know, but beyond you two, there aren’t many people who could benefit from this experiment. When you start an experiment, you should note how generalizable your findings may be if they are confirmed. Generalizability is basically how common a particular phenomenon is to other people’s everyday life.
Let’s say you’re asking a question about the health benefits of eating an apple for one day only, you need to realize that the experiment may be too specific to be helpful. It does not help to explain a phenomenon that many people experience. If you find yourself with too specific of a hypothesis, go back to asking the big question: what is it that you want to know, and what do you think will happen between your two variables?
We know it can be hard to write a good hypothesis unless you’ve seen some good hypothesis examples. We’ve included four hypothesis examples based on some made-up experiments. Use these as templates or launch pads for coming up with your own hypotheses.
You are a student at PrepScholar University. When you walk around campus, you notice that, when the temperature is above 60 degrees, more students study in the quad. You want to know when your fellow students are more likely to study outside. With this information, how do you make the best hypothesis possible?
You must remember to make additional observations and do secondary research before writing your hypothesis. In doing so, you notice that no one studies outside when it’s 75 degrees and raining, so this should be included in your experiment. Also, studies done on the topic beforehand suggested that students are more likely to study in temperatures less than 85 degrees. With this in mind, you feel confident that you can identify your variables and write your hypotheses:
If-then: “If the temperature in Fahrenheit is less than 60 degrees, significantly fewer students will study outside.”
Null: “If the temperature in Fahrenheit is less than 60 degrees, the same number of students will study outside as when it is more than 60 degrees.”
These hypotheses are plausible, as the temperatures are reasonably within the bounds of what is possible. The number of people in the quad is also easily observable. It is also not a phenomenon specific to only one person or at one time, but instead can explain a phenomenon for a broader group of people.
To complete this experiment, you pick the month of October to observe the quad. Every day (except on the days where it’s raining)from 3 to 4 PM, when most classes have released for the day, you observe how many people are on the quad. You measure how many people come and how many leave. You also write down the temperature on the hour.
After writing down all of your observations and putting them on a graph, you find that the most students study on the quad when it is 70 degrees outside, and that the number of students drops a lot once the temperature reaches 60 degrees or below. In this case, your research report would state that you accept or “failed to reject” your first hypothesis with your findings.
Let’s say that you work at a bakery. You specialize in cupcakes, and you make only two colors of frosting: yellow and purple. You want to know what kind of customers are more likely to buy what kind of cupcake, so you set up an experiment. Your independent variable is the customer’s gender, and the dependent variable is the color of the frosting. What is an example of a hypothesis that might answer the question of this study?
Here’s what your hypotheses might look like:
If-then: “If customers’ gender is female, then they will buy more yellow cupcakes than purple cupcakes.”
Null: “If customers’ gender is female, then they will be just as likely to buy purple cupcakes as yellow cupcakes.”
This is a pretty simple experiment! It passes the test of plausibility (there could easily be a difference), defined concepts (there’s nothing complicated about cupcakes!), observability (both color and gender can be easily observed), and general explanation ( this would potentially help you make better business decisions ).
While watching your backyard bird feeder, you realized that different birds come on the days when you change the types of seeds. You decide that you want to see more cardinals in your backyard, so you decide to see what type of food they like the best and set up an experiment.
However, one morning, you notice that, while some cardinals are present, blue jays are eating out of your backyard feeder filled with millet. You decide that, of all of the other birds, you would like to see the blue jays the least. This means you'll have more than one variable in your hypothesis. Your new hypotheses might look like this:
If-then: “If sunflower seeds are placed in the bird feeders, then more cardinals will come than blue jays. If millet is placed in the bird feeders, then more blue jays will come than cardinals.”
Null: “If either sunflower seeds or millet are placed in the bird, equal numbers of cardinals and blue jays will come.”
Through simple observation, you actually find that cardinals come as often as blue jays when sunflower seeds or millet is in the bird feeder. In this case, you would reject your “if-then” hypothesis and “fail to reject” your null hypothesis . You cannot accept your first hypothesis, because it’s clearly not true. Instead you found that there was actually no relation between your different variables. Consequently, you would need to run more experiments with different variables to see if the new variables impact the results.
You’re about to give a speech in one of your classes about the importance of paying attention. You want to take this opportunity to test a hypothesis you’ve had for a while:
If-then: If students sit in the first two rows of the classroom, then they will listen better than students who do not.
Null: If students sit in the first two rows of the classroom, then they will not listen better or worse than students who do not.
You give your speech and then ask your teacher if you can hand out a short survey to the class. On the survey, you’ve included questions about some of the topics you talked about. When you get back the results, you’re surprised to see that not only do the students in the first two rows not pay better attention, but they also scored worse than students in other parts of the classroom! Here, both your if-then and your null hypotheses are not representative of your findings. What do you do?
This is when you reject both your if-then and null hypotheses and instead create an alternative hypothesis . This type of hypothesis is used in the rare circumstance that neither of your hypotheses is able to capture your findings . Now you can use what you’ve learned to draft new hypotheses and test again!
The more comfortable you become with writing hypotheses, the better they will become. The structure of hypotheses is flexible and may need to be changed depending on what topic you are studying. The most important thing to remember is the purpose of your hypothesis and the difference between the if-then and the null . From there, in forming your hypothesis, you should constantly be asking questions, making observations, doing secondary research, and considering your variables. After you have written your hypothesis, be sure to edit it so that it is plausible, clearly defined, observable, and helpful in explaining a general phenomenon.
Writing a hypothesis is something that everyone, from elementary school children competing in a science fair to professional scientists in a lab, needs to know how to do. Hypotheses are vital in experiments and in properly executing the scientific method . When done correctly, hypotheses will set up your studies for success and help you to understand the world a little better, one experiment at a time.
If you’re studying for the science portion of the ACT, there’s definitely a lot you need to know. We’ve got the tools to help, though! Start by checking out our ultimate study guide for the ACT Science subject test. Once you read through that, be sure to download our recommended ACT Science practice tests , since they’re one of the most foolproof ways to improve your score. (And don’t forget to check out our expert guide book , too.)
If you love science and want to major in a scientific field, you should start preparing in high school . Here are the science classes you should take to set yourself up for success.
If you’re trying to think of science experiments you can do for class (or for a science fair!), here’s a list of 37 awesome science experiments you can do at home
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We have heard of many hypotheses which have led to great inventions in science. Assumptions that are made on the basis of some evidence are known as hypotheses. In this article, let us learn in detail about the hypothesis and the type of hypothesis with examples.
A hypothesis is an assumption that is made based on some evidence. This is the initial point of any investigation that translates the research questions into predictions. It includes components like variables, population and the relation between the variables. A research hypothesis is a hypothesis that is used to test the relationship between two or more variables.
Following are the characteristics of the hypothesis:
Following are the sources of hypothesis:
There are six forms of hypothesis and they are:
It shows a relationship between one dependent variable and a single independent variable. For example – If you eat more vegetables, you will lose weight faster. Here, eating more vegetables is an independent variable, while losing weight is the dependent variable.
It shows the relationship between two or more dependent variables and two or more independent variables. Eating more vegetables and fruits leads to weight loss, glowing skin, and reduces the risk of many diseases such as heart disease.
It shows how a researcher is intellectual and committed to a particular outcome. The relationship between the variables can also predict its nature. For example- children aged four years eating proper food over a five-year period are having higher IQ levels than children not having a proper meal. This shows the effect and direction of the effect.
It is used when there is no theory involved. It is a statement that a relationship exists between two variables, without predicting the exact nature (direction) of the relationship.
It provides a statement which is contrary to the hypothesis. It’s a negative statement, and there is no relationship between independent and dependent variables. The symbol is denoted by “H O ”.
Associative hypothesis occurs when there is a change in one variable resulting in a change in the other variable. Whereas, the causal hypothesis proposes a cause and effect interaction between two or more variables.
Following are the examples of hypotheses based on their types:
Following are the functions performed by the hypothesis:
Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:
What is hypothesis.
A hypothesis is an assumption made based on some evidence.
What are the types of hypothesis.
Types of hypothesis are:
Define complex hypothesis..
A complex hypothesis shows the relationship between two or more dependent variables and two or more independent variables.
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The Difference Between Hypothesis and Theory
A hypothesis is an assumption, an idea that is proposed for the sake of argument so that it can be tested to see if it might be true.
In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review. You ask a question, read up on what has been studied before, and then form a hypothesis.
A hypothesis is usually tentative; it's an assumption or suggestion made strictly for the objective of being tested.
A theory , in contrast, is a principle that has been formed as an attempt to explain things that have already been substantiated by data. It is used in the names of a number of principles accepted in the scientific community, such as the Big Bang Theory . Because of the rigors of experimentation and control, it is understood to be more likely to be true than a hypothesis is.
In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch, with theory being the more common choice.
Since this casual use does away with the distinctions upheld by the scientific community, hypothesis and theory are prone to being wrongly interpreted even when they are encountered in scientific contexts—or at least, contexts that allude to scientific study without making the critical distinction that scientists employ when weighing hypotheses and theories.
The most common occurrence is when theory is interpreted—and sometimes even gleefully seized upon—to mean something having less truth value than other scientific principles. (The word law applies to principles so firmly established that they are almost never questioned, such as the law of gravity.)
This mistake is one of projection: since we use theory in general to mean something lightly speculated, then it's implied that scientists must be talking about the same level of uncertainty when they use theory to refer to their well-tested and reasoned principles.
The distinction has come to the forefront particularly on occasions when the content of science curricula in schools has been challenged—notably, when a school board in Georgia put stickers on textbooks stating that evolution was "a theory, not a fact, regarding the origin of living things." As Kenneth R. Miller, a cell biologist at Brown University, has said , a theory "doesn’t mean a hunch or a guess. A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”
While theories are never completely infallible, they form the basis of scientific reasoning because, as Miller said "to the best of our ability, we’ve tested them, and they’ve held up."
hypothesis , theory , law mean a formula derived by inference from scientific data that explains a principle operating in nature.
hypothesis implies insufficient evidence to provide more than a tentative explanation.
theory implies a greater range of evidence and greater likelihood of truth.
law implies a statement of order and relation in nature that has been found to be invariable under the same conditions.
These examples are programmatically compiled from various online sources to illustrate current usage of the word 'hypothesis.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.
Greek, from hypotithenai to put under, suppose, from hypo- + tithenai to put — more at do
1641, in the meaning defined at sense 1a
This is the Difference Between a...
In scientific reasoning, they're two completely different things
hypothermia
hypothesize
“Hypothesis.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/hypothesis. Accessed 1 Aug. 2024.
Kids definition of hypothesis, medical definition, medical definition of hypothesis, more from merriam-webster on hypothesis.
Nglish: Translation of hypothesis for Spanish Speakers
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Britannica.com: Encyclopedia article about hypothesis
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Hypothesis is a testable statement that explains what is happening or observed. It proposes the relation between the various participating variables. Hypothesis is also called Theory, Thesis, Guess, Assumption, or Suggestion. Hypothesis creates a structure that guides the search for knowledge.
In this article, we will learn what is hypothesis, its characteristics, types, and examples. We will also learn how hypothesis helps in scientific research.
Table of Content
Hypothesis meaning, characteristics of hypothesis, sources of hypothesis, types of hypothesis, simple hypothesis, complex hypothesis, directional hypothesis, non-directional hypothesis, null hypothesis (h0), alternative hypothesis (h1 or ha), statistical hypothesis, research hypothesis, associative hypothesis, causal hypothesis, hypothesis examples, simple hypothesis example, complex hypothesis example, directional hypothesis example, non-directional hypothesis example, alternative hypothesis (ha), functions of hypothesis, how hypothesis help in scientific research.
A hypothesis is a suggested idea or plan that has little proof, meant to lead to more study. It’s mainly a smart guess or suggested answer to a problem that can be checked through study and trial. In science work, we make guesses called hypotheses to try and figure out what will happen in tests or watching. These are not sure things but rather ideas that can be proved or disproved based on real-life proofs. A good theory is clear and can be tested and found wrong if the proof doesn’t support it.
A hypothesis is a proposed statement that is testable and is given for something that happens or observed.
Here are some key characteristics of a hypothesis:
Hypotheses can come from different places based on what you’re studying and the kind of research. Here are some common sources from which hypotheses may originate:
Here are some common types of hypotheses:
Simple Hypothesis guesses a connection between two things. It says that there is a connection or difference between variables, but it doesn’t tell us which way the relationship goes.
Complex Hypothesis tells us what will happen when more than two things are connected. It looks at how different things interact and may be linked together.
Directional Hypothesis says how one thing is related to another. For example, it guesses that one thing will help or hurt another thing.
Non-Directional Hypothesis are the one that don’t say how the relationship between things will be. They just say that there is a connection, without telling which way it goes.
Null hypothesis is a statement that says there’s no connection or difference between different things. It implies that any seen impacts are because of luck or random changes in the information.
Alternative Hypothesis is different from the null hypothesis and shows that there’s a big connection or gap between variables. Scientists want to say no to the null hypothesis and choose the alternative one.
Statistical Hypotheis are used in math testing and include making ideas about what groups or bits of them look like. You aim to get information or test certain things using these top-level, common words only.
Research Hypothesis comes from the research question and tells what link is expected between things or factors. It leads the study and chooses where to look more closely.
Associative Hypotheis guesses that there is a link or connection between things without really saying it caused them. It means that when one thing changes, it is connected to another thing changing.
Causal Hypothesis are different from other ideas because they say that one thing causes another. This means there’s a cause and effect relationship between variables involved in the situation. They say that when one thing changes, it directly makes another thing change.
Following are the examples of hypotheses based on their types:
Hypotheses have many important jobs in the process of scientific research. Here are the key functions of hypotheses:
Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:
Mathematics Maths Formulas Branches of Mathematics
A hypothesis is a testable statement serving as an initial explanation for phenomena, based on observations, theories, or existing knowledge. It acts as a guiding light for scientific research, proposing potential relationships between variables that can be empirically tested through experiments and observations.
The hypothesis must be specific, testable, falsifiable, and grounded in prior research or observation, laying out a predictive, if-then scenario that details a cause-and-effect relationship. It originates from various sources including existing theories, observations, previous research, and even personal curiosity, leading to different types, such as simple, complex, directional, non-directional, null, and alternative hypotheses, each serving distinct roles in research methodology .
The hypothesis not only guides the research process by shaping objectives and designing experiments but also facilitates objective analysis and interpretation of data , ultimately driving scientific progress through a cycle of testing, validation, and refinement.
What is a hypothesis.
A guess is a possible explanation or forecast that can be checked by doing research and experiments.
The components of a Hypothesis are Independent Variable, Dependent Variable, Relationship between Variables, Directionality etc.
Testability, Falsifiability, Clarity and Precision, Relevance are some parameters that makes a Good Hypothesis
You cannot prove conclusively that most hypotheses are true because it’s generally impossible to examine all possible cases for exceptions that would disprove them.
Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data
Yes, you can change or improve your ideas based on new information discovered during the research process.
Hypotheses are used to support scientific research and bring about advancements in knowledge.
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What is a hypothesis ? You can think of it as a prediction about what’s going to happen in certain circumstances.
For example, you could say “If I feed my dog natural food, he’ll be healthier.” That’s a very basic hypothesis.
When a scientist forms a hypothesis, it’s usually a little more complicated than our example. A hypothesis used in science will describe what you think will happen in some measurable or testable way.
So our example could become, “If I feed my dog natural food, his skin rash will disappear.”
Let’s look at a dictionary definition : “an assumption or concession made for the sake of argument.”
That’s a great way to describe what a hypothesis is, so we’ll use it for this article.
A hypothesis is a key part of what is called the Scientific Method, which is how scientists study things and make conclusions.
The steps in the scientific method are:
1. Observation of a problem leads to you to want to know more 2. Forming a Hypothesis, or the prediction based on questions you’ve asked 3. Designing an experiment to test the hypothesis 4. Gathering data 5. Forming a conclusion based on the data
By using this method, scientists can decide if their data agree with the hypothesis, or show that it is not correct.
If other scientists can perform the same experiments and get the same results, then the hypothesis can become what is called a theory. What this means is that the hypothesis has been supported by many tests and can be considered to be a reliable explanation of the facts.
As you can see, the hypothesis is a very important step in the process. It guides how the experiment is set up and what data are collected. The conclusion is either did the data support or refute the hypothesis.
The starting point for a hypothesis is usually some observation that makes you ask a question. Let’s think about another example.
Suppose you notice that your neighbor’s flowers are taller and look better than yours. Another neighbor’s plants look worse. What could be causing the difference?
You could interview your neighbors, to find out what they do, or read some tips on gardening.
After doing this research, you might have found out:
• All of you planted your gardens at the same time, using the same garden soil • Since you are neighbors, you all have the same weather conditions and the same amount of rain • The only difference seems to be that one neighbor uses plant food.
Now we might have more questions. One that comes to mind is “How much plant food will work the best?”
Using your research and the new questions you have, you can create your hypothesis. You aren’t just making a guess, you are basing it on your research and observation. You are predicting the solution.
Your hypothesis could be:
If I feed my plants with plant food, they will grow faster.
Notice you have three key elements in your hypothesis.
The first is called the dependent variable. This is the thing you are testing (the growth of your plants.)
The second is called the independent variable. This is the thing you are changing (using plant food, when before you didn’t.)
And the third thing is what you are going to measure to prove your hypothesis. Here, it’s the height of your plants.
If you need more help forming a hypothesis, try this article from some great examples.
Once you have your hypothesis, now you need to test it. Another word for this is experiment.
It’s important when setting up your experiment to know exactly what you are going to measure, and how often.
Taking our example, we could divide our garden into two groups.
Group number 1 is what you would call the control group. You wouldn’t change anything in how you take care of the plants. Just water them the way you have been.
Group number 2 would get water and plant food, according to the directions on the package.
If you grow more than one kind of plant, either assign some of each kind of plant to each of the two groups, or just test one type of plant. It wouldn’t do you any good, for instance, to put tomatoes in one group and marigolds in another.
On Day 1 of your experiment, you would measure the height of the plants in each group and record your findings.
Then repeat your measurements on Days 7, 14 and 21 to see what happened.
At the end of three weeks, you’d look at your data. Which plants grew the best? If it was Group 2, then your hypothesis was supported by the data.
If Group 1 grew faster, then you’ll know the plant food wasn’t really what made your neighbor’s plants grow so well. You’ll have to do some more research and come up with another hypothesis.
Now you know the answer to the question, what is a hypothesis. It’s a prediction of what’s going to happen that you can test or measure.
Forming a hypothesis and testing it is the basis for the scientific method, which is how scientists study things in a systematic way.
What do you think? Are you ready to form your own hypothesis and test it? Or do you have more questions? Leave a comment and let us know.
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What it is and how it's used in sociology
A hypothesis is a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence.
Within social science, a hypothesis can take two forms. It can predict that there is no relationship between two variables, in which case it is a null hypothesis . Or, it can predict the existence of a relationship between variables, which is known as an alternative hypothesis.
In either case, the variable that is thought to either affect or not affect the outcome is known as the independent variable, and the variable that is thought to either be affected or not is the dependent variable.
Researchers seek to determine whether or not their hypothesis, or hypotheses if they have more than one, will prove true. Sometimes they do, and sometimes they do not. Either way, the research is considered successful if one can conclude whether or not a hypothesis is true.
A researcher has a null hypothesis when she or he believes, based on theory and existing scientific evidence, that there will not be a relationship between two variables. For example, when examining what factors influence a person's highest level of education within the U.S., a researcher might expect that place of birth, number of siblings, and religion would not have an impact on the level of education. This would mean the researcher has stated three null hypotheses.
Taking the same example, a researcher might expect that the economic class and educational attainment of one's parents, and the race of the person in question are likely to have an effect on one's educational attainment. Existing evidence and social theories that recognize the connections between wealth and cultural resources , and how race affects access to rights and resources in the U.S. , would suggest that both economic class and educational attainment of the one's parents would have a positive effect on educational attainment. In this case, economic class and educational attainment of one's parents are independent variables, and one's educational attainment is the dependent variable—it is hypothesized to be dependent on the other two.
Conversely, an informed researcher would expect that being a race other than white in the U.S. is likely to have a negative impact on a person's educational attainment. This would be characterized as a negative relationship, wherein being a person of color has a negative effect on one's educational attainment. In reality, this hypothesis proves true, with the exception of Asian Americans , who go to college at a higher rate than whites do. However, Blacks and Hispanics and Latinos are far less likely than whites and Asian Americans to go to college.
Formulating a hypothesis can take place at the very beginning of a research project , or after a bit of research has already been done. Sometimes a researcher knows right from the start which variables she is interested in studying, and she may already have a hunch about their relationships. Other times, a researcher may have an interest in a particular topic, trend, or phenomenon, but he may not know enough about it to identify variables or formulate a hypothesis.
Whenever a hypothesis is formulated, the most important thing is to be precise about what one's variables are, what the nature of the relationship between them might be, and how one can go about conducting a study of them.
Updated by Nicki Lisa Cole, Ph.D
Science has a good understanding of why and how human consciousness evolved..
Posted July 29, 2024 | Reviewed by Margaret Foley
Is the “stoned ape theory” of human consciousness valid? This is the hypothesis that consuming psychedelic mushrooms played a major role in the evolution of human consciousness. It was put forth by Terence McKenna in Food of the Gods: The Search for the Original Tree of Knowledge , and favorably mentioned by Joe Rogan .
Recently a published study wove some interesting puzzle pieces together to suggest that McKenna's speculation might have had some merit. The study was picked up by Popular Mechanics and other news outlets with announcements like, “ Scientists say they may have discovered origin of consciousness ”!
As a scientist, let me tell you that headlines like these are irresponsible. Is it possible that consuming magic mushrooms played a role in the evolution of human consciousness? Of course. Many things are possible. However, it is not terribly plausible, nor is it clear how this would have happened. McKenna tells an interesting story, but that is pretty much all we are looking at. As such, it is a weak evolutionary idea, one that is entertaining and could have some validity but is probably more misleading than useful. The recent study lends some weak support to it, but not much. As is so often the case, the problem is much more in the headlines than the study.
Before we even consider something like the stoned ape theory, we need to back up and get clear about what know about human consciousness and how it is distinct from other animals and how it likely evolved. Failure to do this results in confusion about what one is trying to explain. For example, animals like dogs obviously have consciousness in the sense that they have inner experiences and feel pain. Making this point makes it obvious that we are not trying to explain subjective conscious experience, and that the emphasis is more on "human" than on "consciousness" per se. And it makes it clear that it is absurd that the stoned ape theory is about the "origin of consciousness" as the headline proclaims.
What are the elements that make human intelligence and consciousness distinct? There are three major domains: a) our social intelligence; b) our technological capacities; and c) propositional language. Michael Tomasello , along with many other researchers, has documented that humans have excellent capacities to develop a “we” space. That is, we can quickly determine the intentions of others, and we can develop shared attention . This means that humans can network together in activities like hunting.
It also means we are more likely to develop collective activities, like engaging in dance. Hunting and dancing require tools in the form of weapons and instruments, and humans demonstrate a remarkable capacity to evolve technology. This has been especially the case in the last 100,000 years. It is the central driver in the evolution of civilizations, which is becoming ever more apparent with our invention of AI .
Last, humans have propositional language. To be sure, other animals have rich capacities for communication. Elephants, killer whales, dolphins, and other great apes can be argued to show the basic elements of symbolic communication. However, humans have an open, symbolic, syntactical language; put more simply, humans talk via propositional language (i.e., in sentences).
What happened was that there was a tipping point from broken symbolic language into propositions. Concretely, this is the difference between going from “antelope…there” to “There are the antelope.” This is crucial because propositions carry what is called "positive meaning" because they make a truth or value claim. These assertions can then be challenged. How? With questions, such as who, what, when, how, and the most crucial “why.”
The fact that we can question the validity of propositions results in a massively complex problem. What truth and value claims should a group or individual adopt? This "question-answer dynamic" is called the problem of justification. Ultimately, it means that humans need to justify themselves at the individual and group level.
The idea that propositional language created the adaptive problem of justification is called the Justification Hypothesis (Henriques, 2003). Notice that it is clear, logical, and not only possible, but clearly plausible, almost to the point of being logically inevitable. However, at this stage in the argument, it remains a hypothesis because we have not given any evidence for it. To do so, we can ask: If it were the case that humans were confronted with this core problem of justification several hundred thousand years ago, then what follows from it?
The answer is that we should see design features in human consciousness that have been shaped by the problem of justification. In addition, we should also see it in human culture. When we turn to our lives, we can then see immediately that this is true (Henriques, 2011; Shaffer, 2005; Quackenbush, 2005). Humans constantly are justifying themselves to themselves and to others. And human cultures can be framed as being tied together by systems of justification. We can also see that this is true in what scientific psychology has revealed about human consciousness and social processes.
Justification Systems Theory (Henriques & Michalski, 2019) ties these ideas together and explains why human consciousness is different from consciousness in other animals. Indeed, please try to doubt it. Raise the question that it might be something else, and see what happens to your consciousness. You will find yourself in a stream of justification. Why? Because human nature comes with a mental organ of justification (Henriques, 2003).
Finally, we can see this visually. Justification Systems Theory is part of a larger system, called UTOK, the Unified Theory of Knowledge (Henriques, 2022), which gives us a unified framework for natural science, human consciousness, and the collective wisdom traditions. Here is UTOK’s Tree of Knowledge System (Henriques, 2003; Henriques and Volk, 2023).
The red layer is the Mind-Animal plane. The blue is the Culture-Person plane. Some time between 500,000 and 50,000 years ago, our ancestors developed the capacity for propositional language, resulting in the problem of justification. That in turn drove human consciousness and culture, which is a whole new plane of existence. Justification Systems Theory is the theory that explains how we go from minded animals to cultured persons.
You are a cultured person who justifies your actions on the social stage. That is what makes human consciousness unique, and the central event was when propositional language gave rise to question-answer dynamics and the adaptive problem of justification. And the theory has been scientifically known for more than two decades. We just need to look at the updated, scientifically informed Tree of Knowledge to see it.
Henriques, G. (2022). A new synthesis for solving the problem of psychology: Addressing the Enlightenment Gap . Palgrave MacMillan.
Henriques, G. R. (2011). A new unified theory of psychology . New York: Springer.
Henriques, G. R. (2003). The tree of knowledge system and the theoretical unification of psychology. Review of General Psychology, 7, 150-182.
Henriques, G. R. & Michalski, J. (2019). Defining behavior and its relationship to the science of psychology. Integrative Psychological and Behavioral Science 54(1), 1-26.
Henriques, G, & Volk, T. (2023). Toward a Big History 2.0. The Journal of Big History, 6(3), pp. 1-4.
Quackenbush, S. (2005). Remythologizing culture: Narrativity, justification, and the politics of personalization. Journal of Clinical Psychology. 61
Shaffer LS. (2005). From mirror self-recognition to the looking-glass self: exploring the Justification Hypothesis. J Clin Psychol. 2005 Jan;61(1):47-65
Gregg Henriques, Ph.D. , is a professor of psychology at James Madison University.
Sticking up for yourself is no easy task. But there are concrete skills you can use to hone your assertiveness and advocate for yourself.
By Deborah Borfitz
August 1, 2024 | Soaring rates of youth prediabetes and diabetes over the past few decades have prompted a multidisciplinary team of experts from the Icahn School of Medicine at Mount Sinai (ISMMS) to create a dataset unifying potentially important clues about how to personalize interventions and devise better prevention strategies. Type 2 diabetes and its precursor, prediabetes, are complicated, seemingly intractable health conditions, in part because interventions haven’t been tailored to the needs of different subgroups of patients, according to Nita Vangeepuram, M.D., MPH, associate professor of pediatrics, population health science and policy, and environmental medicine and climate science.
It wasn’t until early in the 21st century that type 2 diabetes was even thought of as a condition afflicting youth, she says. That realization has been attributed to growing rates of obesity and declines in healthy eating and activity levels in children and adolescents.
Given that the combinations of disease risk factors can differ significantly from person to person, it follows that more nuanced treatment regimens are needed to stem the epidemic, Vangeepuram says. However, until now, progress has been hampered by the lack of a comprehensive and accessible dataset providing a detailed view of the vulnerabilities and trends.
That all changed with the recent launch of the study’s dataset and publicly available POND (prediabetes/diabetes in youth online dashboard), both enabled by the availability of National Health and Nutrition Examination Survey (NHANES) data collected from 1999 to 2018 by the Centers for Disease Control and Prevention. It was a multi-year, multidisciplinary effort combining the expertise of Vangeepuram, a clinician and child health equity researcher, with the machine learning knowhow of Gaurav Pandey, Ph.D. and epidemiology knowledge of Bian Liu, Ph.D.
More than two dozen variables—race and ethnicity, health insurance, body mass index (BMI), and screen time among them—were identified as being significantly correlated with youth prediabetes and diabetes in an article about the dataset and POND that was published recently in JMIR Public Health and Surveillance ( DOI: 10.2196/53330 ). Across different statistical and machine learning methods, “the same story played out over and over again,” notes Vangeepuram.
Known and Novel Associations
The analysis found both known and novel variables that matter in terms of disease risk. Most surprising, though, is “that there were associations across all the different domains [sociodemographic, health status, diet, and lifestyle behaviors] included in the dataset,” Vangeepuram says. “This makes the case that we need to do more research to figure out what groups are at risk, which risk factors are important for whom, and what we do about it.”
A pair of case studies included in the published paper point to the potential of the dataset for translational studies, says Liu, associate professor of population health science and policy, and environmental medicine and climate science at ISMMS. One identified 27 individual variables associated with prediabetes and diabetes status and the other predicted that status using machine learning.
Both the statistical bivariate analyses and a machine learning framework known as Ensemble Integration (El) identified 27 risk-associated variables, 16 of which overlapped: gender, food stamps, race and ethnicity, and insurance (sociodemographic); weight, height, waist circumference, BMI, taking prescription drugs, hypertension, and general health (health status); total protein, oils, fruits, and solid fat consumed over 24 hours (diet); and screen time (other lifestyle behaviors). The El approach also identified 11 additional predictive variables, including some known (e.g., meat and fruit intake and family income) and other less recognized factors (e.g., number of rooms in homes), the study team reports.
It is believed to be the first such dataset of its kind for youth prediabetes and diabetes, says Liu. A preliminary search turned up only collections focused on adult-onset diabetes or comprised essentially of genomic data.
Inspiration for creating POND comes from web portals in the genomics field, such as The Cancer Genome Atlas and Genomics 2 Proteins Portal, says Pandey, Ph.D., associate professor of genetics and genomic sciences, and artificial intelligence and human health at ISMMS. However, unlike POND, these online platforms aren’t tied to the daily life realities and risk factors of diabetes among youth.
NHANES data, while available on the internet, requires some expert knowledge and extensive effort to properly analyze and be of practical value to most would-be users, Liu says. The Mount Sinai team spent several years cleaning and harmonizing the data, selecting the variables for the case studies, and getting the information into a user-friendly format for the POND web portal so it would be of value to other hypothesis-testing researchers as well as healthcare professionals and the public.
Through this part of the work, the team aims to illustrate the utility of data sharing, transparent methodology and code, and public data portals to the epidemiology and public health communities, says Pandey.
Personalizing Prevention Interventions
It is well known that type 2 diabetes and prediabetes disproportionately affect racially and ethnically minoritized populations across the lifespan, says Vangeepuram. These are also understood to be multifactorial conditions, although not all the risk-imparting variables have been identified, nor the extent of their impact on different subpopulations.
Although type 2 diabetes has conventionally been thought of as an adult disease, rising prevalence of the disease and related metabolic conditions in youth has drawn attention to the differing burden of complications they face, says Liu. Preventing the progression of prediabetes into full-blown diabetes with all its unwelcome health consequences was one of the initial motivations for creating the dataset and POND.
Vangeepuram sees all this firsthand in her clinical practice. “When you diagnose these conditions early on in life, there is a much greater chance that [patients will] develop diabetes-related complications, of which there are many—eye disease, kidney disease, amputations, and cardiovascular risks—so the earlier you can intervene the better.”
Interventions to date have been “more cookie cutter” than personalized to patients based on their differing characteristics, Vangeepuram continues, adding that she is particularly excited to explore subgroup differences to inform prevention efforts. These are not the kinds of things that are easily addressed in the context of a 15-minute well child visit. With so many topics to cover, ranging from needed vaccines and issues around regular growth and development, often little time remains for pediatricians to delve into a child’s struggle with weight and risk of diabetes, and other metabolic conditions.
Translation Potential
The work of the Mount Sinai team began with the processing of NHANES data from 10 survey cycles (1999-2018), which yielded a dataset covering 15,149 youths with known prediabetes or diabetes status (yes or no) based on fasting plasma glucose and hemoglobin A1C biomarkers, says Liu. They then selected 27 potentially relevant NHANES questionnaires grouped by domain—sociodemographic (demographic, socioeconomic, and social determinants of health variables such as age, gender, poverty status, and food security), health status (e.g., blood pressure, total cholesterol, and BMI), diet (e.g., meals eaten per week and added sugar intake), and other lifestyle behaviors (physical activity, screen time, and exposure to secondhand smoke).
From the selected questionnaires, investigators next extracted 95 potentially relevant variables, and through statistical analyses and machine learning determined which were most associated with youth prediabetes and diabetes risk. Nearly a year was spent creating POND to enable any user with an internet connection to navigate, visualize, and download the data themselves, says Pandey, adding that the user-friendly web portal includes the code used to generate the data, in addition to the two case studies. The code to build POND is also publicly available on the portal, which the team hopes will enable similar systems in public health, epidemiology, and beyond.
Since the study population was derived from NHANES, which is “quite representative of the problem and the population” across the country, Pandey says, the utility of the dataset and POND should remain for years to come. “Our goal is to enable data-driven discovery in the field like never before.”
POND isn’t expected to require much IT support from his lab, says Pandey. The team is also happy to field questions via email, as needed, to facilitate usage by outside groups looking to help fill the research gaps.
The dataset is not without its limitations, Liu says, since it was drawn from a cross-sectional survey. The prediabetes and diabetes status and related variables included in the dataset provide only snapshots of youth in the U.S. over the available, two-year NHANES survey cycles, making the identified associations best suited for hypothesis generation purposes that get tested in prospective longitudinal and randomized trials.
Vangeepuram’s focus is squarely on the important, persistent, and worsening public health problem these childhood conditions represent across entire lifespans. Youth prediabetes and diabetes have huge implications for the health and economy of the nation, she says, so “we have to use the information we have to start figuring out what we can do about it.”
Given the transparency around the process used to develop the dataset and web portal these resources could be useful for different datasets for other health topics, she adds. For example, the methodology might be used to construct a similar dataset and portal specific to youth mental health to better prevent and treat depression, anxiety, and suicidal ideation.
By Laura Baisas
Posted on Jul 30, 2024 9:00 AM EDT
5 minute read
Their ethereal yellow (or green, or red) glow popping up from the grass signals summer. While there may be as many as 2,400 species of firefly on Earth, these flying, bioluminescent insects are under threat. Worsening habitat conditions might mean more summers with less fireflies.
Recently, scientists also began to question the prevailing hypothesis for why they evolved their glow in the first place. New analysis on a genetic level could be crucial for helping fireflies face their survival challenges, since they may need to evolve yet again.
[Related: How do fireflies power their blinking butts? ]
For years, scientists believed that the bright lights emitted from fireflies in the Lampyrida e family first evolved as a warning signal to predators. Their signature glow would signal that they were toxic and probably won’t taste very good. Over time, that light was potentially repurposed as a mating signal.
In a recent study published in the journal PNAS Nexus , a team in China put this to the test by modeling out a firefly family tree with genetic data. They traced the evolution of the chemical compounds that make the fireflies toxic– lucibufagins . They collected samples of 16 species of Lampyridae from across China and with two related species. In addition to running a full genetic analysis of each species, they also looked for lucibufagins using liquid chromatography-mass spectrometry . This process is used in a lab to separate, identify, and measure what substances are present in a liquid sample.
They found that lucibufagin toxins are present in only one subfamily of fireflies and that it may have evolved later. However, bioluminescence itself without this toxin is found widely across the entire family. According to the team , this suggests that the toxin evolved after bioluminescence itself evolved in animals and in response to something other than mating.
Instead, their glow may have evolved due to a particularly stressful situation. An ingredient firefly bioluminescence called luciferin has antioxidant properties. The study found that firefly ancestors evolved and diversified when atmospheric oxygen levels rose following a historical low after the Toarcian Oceanic Anoxic Event . This mass extinction event about 183 million years ago during the Early Jurassic caused a widespread decrease in oxygen in the world’s oceans.
[Related: Mass extinction 183 million years ago offers dire warning for modern oceans .]
Luciferin may have been a way for the fireflies to handle less oxygen in the air. Glowing millipedes are also believed to have evolved bioluminescence as a way to cope with oxidative stress in hot, dry environments. Fireflies may have also followed a similar path in response to the stress of oxygen depletion.
While fireflies may have evolved this glow as a response to low oxygen millions of years ago, what does that mean in the face of today’s environmental challenges? They generally thrive in temperate conditions . Warm and wet summers create their ideal breeding environment, with cold winters supporting the survival of their eggs, larvae, and pupae.
As global temperatures continue to rise, these climate conditions are becoming less predictable . For the fireflies and numerous other species, it means that the planet is often less hospitable. Changes in precipitation patterns have also contributed to either severely dry conditions that reduce larval survival or a scenario with too much water that can flood their breeding grounds and disrupt life cycles.
A study published in the June 2024 edition of Science of the Total Environment suggests that they are sensitive to several environmental factors, including short-term weather conditions, longer climatic trends, and urban encroachment.
“Subtle changes in climate patterns, especially related to temperature, are significantly impacting firefly breeding cycles and habitat quality,” study co-author and University of Kentucky ecologist Darin McNeil said in a statement.
Using AI-based machine learning techniques, the team analyzed over 24,000 surveys conducted by citizen science initiative Firefly Watch–now called Firefly Atlas . The analysis revealed that not only is the planet’s incredibly wild weather patterns harming fireflies, but human encroachment is as well. They found that fireflies are less common in areas that have significant light pollution at night .
[Related: Why artificial light—and evolution—trap moths. ]
However, they found that the recent decline in firefly populations is not uniform across all species or geographic regions. Some have adapted to drier environments or those with specific breeding patterns may not be as affected by certain changes, while others are more vulnerable. This pattern shows just how complex natural ecosystems are and why conservation strategies are not simple one-size-fits-all solutions.
While not considered endangered, their numbers are dwindling.
“I would say fireflies are threatened due to habitat loss, but they are not going extinct, some are adapting in different regions,” entomologist Eric Day told Virginia Tech News .
To help, entomologists recommend providing fireflies with some additional habitat incorporating wild or native species of plants in your yard or garden. The native plant finder website from University of Delaware entomologist Doug Tallamy is a great resource to find out which plants are native to your area. Gardeners and homeowners should also avoid using pesticides whenever possible. They can not only degrade habitats, but kill fireflies and their prey.
Turning off superfluous outdoor lights, especially during the summer, can also help. These lights not only confuse fireflies, but moths and other nocturnal species .
“This is why we’re seeing less and less each year. The more development there is, the less room there is for them to thrive,” said Day. “The presence of fireflies indicates a diverse habitat and doing these things is essential to ensuring future generations can enjoy the natural wonder of skies lit up by them.”
How everyone can help monarch butterflies how everyone can help monarch butterflies, there’s a right and a wrong way to build a solar farm there’s a right and a wrong way to build a solar farm.
By Andrew Paul
Did we really take the red pill, the blue pill.
Could we be trapped inside a simulated reality, rather than the physical universe we usually assume?
It's a tantalizing theory, long theorized by philosophers and popularized by the 1999 blockbuster "The Matrix." What if there was a way to find out once and for all if we're living inside a computer?
A former NASA physicist named Thomas Campbell has taken it upon himself to do just that. He devised several experiments, as detailed in a 2017 paper published in the journal The International Journal of Quantum Foundations , designed to detect if something is rendering the world around us like a video game.
Now, scientists at the California State Polytechnic University (CalPoly) have gotten started on the first experiment, putting Campbell's far-fetched hypothesis to the test.
And Campbell has set up an entire non-profit called Center for the Unification of Science and Consciousness (CUSAC) to fund these endeavors. The experiments are "expected to provide strong scientific evidence that we live in a computer-simulated virtual reality," according to a press release by the group.
Needless to say, it's an eyebrow-raising project. As always, extraordinary claims will require extraordinary evidence — but regardless, it's a fun idea.
Campbell's experiments include a new spin on the double-slit experiment, a physics demonstration designed to show how light and matter can act like both waves and particles.
Campbell believes that by removing the observer from these experiments, the actual recorded information never existed in the first place. That's instead of current quantum physics suggesting the existence of entanglement that links particles across a distance.
In simple terms, without a player, the universe around them doesn't exist, much like a video game — proof, in Campbell's thinking , that the universe is exclusively "participatory."
Campbell isn't the first to explore a simulation hypothesis. Back in 2003, Swedish philosopher Nick Bostrom published a paper titled " Are You Living in a Computer Simulation? "
Basically, his idea was that if we progress far enough technologically, we'll probably end up running a simulation of our ancestors. Give those simulated ancestors enough time, and they'll end up simulating their own ancestors. Eventually, most minds in existence will be inside layers of simulations — meaning that we probably are too.
Campbell's hypothesis takes a different tack than Bostrom's "ancestor simulation," arguing that our "consciousness is not a product of the simulation — it is fundamental to reality," in CUSAC's press release.
If he were to be successful in his bid to prove that humanity is trapped in a virtual reality — an endeavor that would subvert our basic understanding of the world around us — it could have major implications.
Campbell argued that the five experiments could "challenge the conventional understanding of reality and uncover profound connections between consciousness and the cosmos."
More on the simulation hypothesis: Famous Hacker Thinks We're Living in Simulation, Wants to Escape
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Title: a black swan hypothesis in markov decision process via irrationality.
Abstract: Black swan events are statistically rare occurrences that carry extremely high risks. A typical view of defining black swan events is heavily assumed to originate from an unpredictable time-varying environments; however, the community lacks a comprehensive definition of black swan events. To this end, this paper challenges that the standard view is incomplete and claims that high-risk, statistically rare events can also occur in unchanging environments due to human misperception of their value and likelihood, which we call as spatial black swan event. We first carefully categorize black swan events, focusing on spatial black swan events, and mathematically formalize the definition of black swan events. We hope these definitions can pave the way for the development of algorithms to prevent such events by rationally correcting human perception
Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
Cite as: | [cs.AI] |
(or [cs.AI] for this version) | |
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hypothesis. science. scientific hypothesis, an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an "If…then" statement summarizing the idea and in the ...
A hypothesis (plural hypotheses) is a proposed explanation for an observation. The definition depends on the subject. In science, a hypothesis is part of the scientific method. It is a prediction or explanation that is tested by an experiment. Observations and experiments may disprove a scientific hypothesis, but can never entirely prove one.
A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method. Many describe it as an "educated guess ...
Theory vs. Hypothesis: Basics of the Scientific Method. Written by MasterClass. Last updated: Jun 7, 2021 • 2 min read. Though you may hear the terms "theory" and "hypothesis" used interchangeably, these two scientific terms have drastically different meanings in the world of science. Explore.
The hypothesis of Andreas Cellarius, showing the planetary motions in eccentric and epicyclical orbits. A hypothesis (pl.: hypotheses) is a proposed explanation for a phenomenon.For a hypothesis to be a scientific hypothesis, the scientific method requires that one can test it. Scientists generally base scientific hypotheses on previous observations that cannot satisfactorily be explained with ...
Developing a hypothesis (with example) Step 1. Ask a question. Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project. Example: Research question.
hypothesis, something supposed or taken for granted, with the object of following out its consequences (Greek hypothesis, "a putting under," the Latin equivalent being suppositio ). Discussion with Kara Rogers of how the scientific model is used to test a hypothesis or represent a theory. Kara Rogers, senior biomedical sciences editor of ...
Definition: Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation. Hypothesis is often used in scientific research to guide the design of experiments ...
A hypothesis is a supposition or tentative explanation for (a group of) phenomena, (a set of) facts, or a scientific inquiry that may be tested, verified or answered by further investigation or methodological experiment. It is like a scientific guess. It's an idea or prediction that scientists make before they do experiments.
A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process. Consider a study designed to examine the relationship between sleep deprivation and test ...
Science is a systematic and logical approach to discovering how things in the universe work. Scientists use the scientific method to make observations, form hypotheses and gather evidence in an ...
A hypothesis is an educated guess, based on observation. It's a prediction of cause and effect. Usually, a hypothesis can be supported or refuted through experimentation or more observation. A hypothesis can be disproven but not proven to be true. Example: If you see no difference in the cleaning ability of various laundry detergents, you might ...
Hypothesis Versus Theory . Although in common usage the terms hypothesis and theory are used interchangeably, the two words mean something different from each other in science. Like a hypothesis, a theory is testable and may be used to make predictions. However, a theory has been tested using the scientific method many times.
A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes - specificity, clarity and testability. Let's take a look at these more closely.
Students learned that it is important that a good hypothesis makes a claim about the relationship between two variables, and that this relationship is specific and testable in a measurable way. Students also learned that only one variable—the independent variable—can differ between test groups. Finally, we talked about how it is important ...
The hypothesis is often written using the words "IF" and "THEN." For example, "If I do not study, then I will fail the test." The "if' and "then" statements reflect your independent and dependent variables. The hypothesis should relate back to your original question and must be testable.
Hypothesis is a prediction of the outcome of a study. Hypotheses are drawn from theories and research questions or from direct observations. In fact, a research problem can be formulated as a hypothesis. To test the hypothesis we need to formulate it in terms that can actually be analysed with statistical tools.
Merriam Webster defines a hypothesis as "an assumption or concession made for the sake of argument.". In other words, a hypothesis is an educated guess. Scientists make a reasonable assumption--or a hypothesis--then design an experiment to test whether it's true or not.
Functions of Hypothesis. Following are the functions performed by the hypothesis: Hypothesis helps in making an observation and experiments possible. It becomes the start point for the investigation. Hypothesis helps in verifying the observations. It helps in directing the inquiries in the right direction.
hypothesis: [noun] an assumption or concession made for the sake of argument. an interpretation of a practical situation or condition taken as the ground for action.
Hypothesis is a testable statement that explains what is happening or observed. It proposes the relation between the various participating variables. Hypothesis is also called Theory, Thesis, Guess, Assumption, or Suggestion. Hypothesis creates a structure that guides the search for knowledge. In this article, we will learn what is hypothesis ...
How a Hypothesis is Used Science. A hypothesis is a key part of what is called the Scientific Method, which is how scientists study things and make conclusions. The steps in the scientific method are: 1. Observation of a problem leads to you to want to know more 2. Forming a Hypothesis, or the prediction based on questions you've asked 3.
A hypothesis is a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence. Within social science, a hypothesis can ...
If his hypothesis is true, it means the seemingly random fluctuations in the abundance of primes are bounded, leaving no big clumps or gaps in their distribution along the number line. Any proof of the Riemann hypothesis would be a window into the secret clockwork governing the primes' irregular pattern.
Science News. from research organizations. ... To test this hypothesis, the researchers investigated the link between PUFA metabolites in umbilical cord blood and ASD scores in 200 children. The ...
Science has a good understanding of why and how human consciousness evolved. ... This is the hypothesis that consuming psychedelic mushrooms played a major role in the evolution of human ...
The Mount Sinai team spent several years cleaning and harmonizing the data, selecting the variables for the case studies, and getting the information into a user-friendly format for the POND web portal so it would be of value to other hypothesis-testing researchers as well as healthcare professionals and the public.
Recently, scientists also began to question the prevailing hypothesis for […] Search for: Science. Archaeology; Biology; Dinosaurs; Physics; ... Laura is a science news writer, covering a wide ...
Simulation Hypothesis Campbell's experiments include a new spin on the double-slit experiment, a physics demonstration designed to show how light and matter can act like both waves and particles.
Black swan events are statistically rare occurrences that carry extremely high risks. A typical view of defining black swan events is heavily assumed to originate from an unpredictable time-varying environments; however, the community lacks a comprehensive definition of black swan events. To this end, this paper challenges that the standard view is incomplete and claims that high-risk ...