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## Hypothesis Testing | A Step-by-Step Guide with Easy Examples

Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.

There are 5 main steps in hypothesis testing:

- State your research hypothesis as a null hypothesis and alternate hypothesis (H o ) and (H a or H 1 ).
- Collect data in a way designed to test the hypothesis.
- Perform an appropriate statistical test .
- Decide whether to reject or fail to reject your null hypothesis.
- Present the findings in your results and discussion section.

Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.

## Table of contents

Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.

After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.

The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.

- H 0 : Men are, on average, not taller than women. H a : Men are, on average, taller than women.

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For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.

There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).

If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.

Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.

Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .

- an estimate of the difference in average height between the two groups.
- a p -value showing how likely you are to see this difference if the null hypothesis of no difference is true.

Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.

In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.

In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).

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The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .

In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.

In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.

However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.

If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”

These are superficial differences; you can see that they mean the same thing.

You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.

If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

- Normal distribution
- Descriptive statistics
- Measures of central tendency
- Correlation coefficient

Methodology

- Cluster sampling
- Stratified sampling
- Types of interviews
- Cohort study
- Thematic analysis

Research bias

- Implicit bias
- Cognitive bias
- Survivorship bias
- Availability heuristic
- Nonresponse bias
- Regression to the mean

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

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## Top 10 XLSTAT Features Every Student Should Know About

Excel has long been a trusted tool among students and professionals for data analysis due to its familiar interface and versatile functionalities. However, its native capabilities sometimes fall short when dealing with more complex statistical analyses and advanced data visualization. This is where add-ins like assistance with XLSTAT assignments comes into play, transforming Excel into powerful statistical analysis software with enhanced features tailored to meet the needs of students. XLSTAT serves as an invaluable extension to Microsoft Excel, offering a comprehensive suite of tools designed to streamline data analysis, visualization, and interpretation. Its seamless integration with Excel ensures a smooth transition for users, allowing them to leverage their existing knowledge of Excel while accessing a plethora of advanced statistical functionalities. Whether you're a student grappling with assignments or a seasoned researcher diving deep into datasets, mastering the key features of XLSTAT can significantly augment your analytical capabilities and streamline your workflow. In this blog post, we'll delve into the top 10 XLSTAT features that every student should be familiar with, highlighting how these features can be effectively utilized to tackle assignments and derive meaningful insights from data. From data import and management to hypothesis testing and beyond, XLSTAT offers a diverse array of tools to support students at every stage of their data analysis journey.

To begin with, XLSTAT simplifies the process of importing and managing data from various sources, including Excel spreadsheets, CSV files, and databases. Its user-friendly interface allows students to effortlessly organize and preprocess datasets, ensuring data cleanliness and integrity before analysis. This feature is particularly valuable for students who often encounter messy or incomplete datasets in their assignments, enabling them to clean and transform data with ease. Furthermore, XLSTAT offers a wide range of descriptive statistical tools, allowing students to summarize and interpret datasets effectively. From basic measures of central tendency to more advanced analyses of variance and distribution, XLSTAT provides students with the tools they need to gain valuable insights into their data. This feature is indispensable for students looking to understand the underlying patterns and trends within their datasets, helping them make informed decisions and draw meaningful conclusions from their analyses. In addition to descriptive statistics, XLSTAT also boasts a robust suite of hypothesis testing tools, including parametric and nonparametric tests for assessing the significance of relationships and differences within datasets. Whether you're comparing means across multiple groups or analyzing categorical data, XLSTAT offers a variety of statistical tests to support your research hypotheses effectively. This feature is particularly valuable for students conducting experiments or surveys as part of their assignments, enabling them to rigorously test their hypotheses and validate their findings.

## Data Cleaning and Preparation

Before embarking on any statistical analysis journey, it's paramount to ensure that the data under scrutiny is devoid of inconsistencies and prepared in a manner conducive to meaningful analysis. XLSTAT, with its array of features, plays a pivotal role in this crucial stage of data analysis. From handling missing values to detecting outliers and transforming data, XLSTAT equips students with robust tools to streamline the data cleaning and preparation process, laying a solid foundation for subsequent analyses.

## Data Cleaning Tools

XLSTAT boasts a repertoire of tools specifically designed to address data cleaning challenges commonly encountered by students. Among these tools are sophisticated techniques for handling missing values and identifying outliers, both of which are essential for maintaining the integrity and reliability of the data. Missing value imputation techniques offered by XLSTAT enable students to effectively handle instances where data points are absent, ensuring that gaps in the dataset do not compromise the validity of subsequent analyses.

By employing advanced imputation methods such as mean imputation, regression imputation, or predictive modeling, students can intelligently fill in missing values without introducing bias into the dataset. Furthermore, XLSTAT provides robust outlier detection methods that empower students to identify and address data points that deviate significantly from the norm. Outliers have the potential to skew statistical analyses and distort the interpretation of results. XLSTAT's outlier detection tools enable students to pinpoint these aberrant data points and decide whether to remove them or employ appropriate data transformation techniques to mitigate their impact.

## Data Preparation Techniques

In addition to cleaning, XLSTAT offers a plethora of data preparation techniques aimed at optimizing the dataset for subsequent analyses. These techniques, including data transformation and normalization, play a pivotal role in ensuring that the data adheres to the assumptions underlying various statistical tests and models, thereby enhancing the robustness and accuracy of the results. Data transformation techniques provided by XLSTAT enable students to modify the distributional properties of variables, making them more amenable to statistical analysis. Common transformations include logarithmic transformation, square root transformation, and Box-Cox transformation, which can help stabilize variance, achieve normality, or satisfy other distributional assumptions required by statistical tests.

Normalization, another essential data preparation technique, involves rescaling variables to a standard scale, typically ranging from 0 to 1 or -1 to 1. XLSTAT offers various normalization methods, including min-max normalization and z-score normalization, allowing students to standardize variables and remove scale-related biases that could affect the results of their analyses. By leveraging these data preparation techniques offered by XLSTAT, students can ensure that their datasets are meticulously curated and optimized for the specific statistical analyses they intend to conduct. This proactive approach to data cleaning and preparation not only enhances the validity and reliability of their findings but also instills confidence in the results obtained from their analyses.

## Descriptive Statistics and Data Visualization

Descriptive statistics are fundamental for understanding the characteristics of a dataset, and visualizing data can provide insights that raw numbers alone cannot convey. XLSTAT offers a range of descriptive statistics and data visualization tools to help students explore and summarize their data effectively.

## Descriptive Statistics

Descriptive statistics provide a snapshot of the key features of a dataset, enabling students to gain insights into its underlying properties. XLSTAT offers a comprehensive set of descriptive statistics, including measures of central tendency, dispersion, and distribution. Measures of central tendency, such as the mean, median, and mode, offer students a way to understand the typical or central value of their data. These measures help students identify where the bulk of their data lies and provide a point of reference for further analysis. Dispersion measures, such as the standard deviation and variance, quantify the spread or variability of the data around the central value.

Understanding dispersion is crucial as it indicates how much individual data points deviate from the mean, providing insights into the variability and consistency of the dataset. Distribution measures, such as skewness and kurtosis, describe the shape of the data distribution. Skewness measures the asymmetry of the distribution, indicating whether the data is skewed to the left or right. Kurtosis measures the peakedness or flatness of the distribution, providing insights into the tails of the distribution and the presence of outliers. By leveraging these descriptive statistics in XLSTAT, students can summarize the key characteristics of their data, gaining a deeper understanding of its structure and variability.

## Data Visualization

Visualizing data is essential for identifying patterns, trends, and outliers that may not be apparent from raw numbers alone. XLSTAT offers various visualization tools that allow students to explore their data visually and uncover hidden insights. Histograms are a commonly used visualization tool that displays the distribution of a continuous variable. By dividing the data into intervals or bins and plotting the frequency of observations within each bin, histograms provide students with a visual representation of the data distribution, allowing them to identify patterns and assess the shape of the distribution. Scatter plots are another valuable visualization tool that allows students to explore the relationship between two continuous variables. By plotting one variable on the x-axis and another variable on the y-axis, scatter plots enable students to identify trends, correlations, and outliers within their data, facilitating a deeper understanding of the underlying relationships.

Box plots, also known as box-and-whisker plots, provide a visual summary of the distribution of a continuous variable. By displaying the median, quartiles, and potential outliers, box plots offer students insights into the central tendency, spread, and variability of their data, making them particularly useful for comparing groups or conditions. By utilizing these visualization tools in XLSTAT, students can explore their data visually, uncover hidden insights, and communicate their findings effectively. Visualizing data not only enhances students' understanding of the dataset but also facilitates the interpretation and communication of their results to others.

## Hypothesis Testing and Statistical Inference

Hypothesis testing is a fundamental concept in statistical analysis, serving as a cornerstone for making informed decisions and drawing conclusions about populations based on sample data. Within the realm of hypothesis testing, XLSTAT stands out as a robust tool that equips students with a diverse array of testing methodologies and analytical capabilities. By facilitating hypothesis testing and statistical inference, XLSTAT empowers students to explore the relationships, differences, and significance within their datasets with confidence and precision.

## Parametric Tests

Parametric tests are statistical tests that rely on specific assumptions about the distribution of data, particularly the assumption of normality. XLSTAT offers a comprehensive suite of parametric tests, including t-tests, analysis of variance (ANOVA), and linear regression, which are widely employed for hypothesis testing across various research scenarios. These parametric tests enable students to evaluate hypotheses regarding population means, variances, and relationships between variables. The t-test, for instance, is commonly used to compare the means of two independent groups or assess the significance of the difference between a sample mean and a known population mean. XLSTAT simplifies the t-test procedure, guiding students through the process of selecting the appropriate test type (e.g., independent samples t-test, paired samples t-test) and interpreting the results with ease.

Similarly, ANOVA, available in XLSTAT, facilitates hypothesis testing when dealing with multiple groups or factors. Whether students are analyzing the effects of different treatments on a dependent variable or exploring the impact of categorical predictors on an outcome, XLSTAT's ANOVA tool provides a comprehensive framework for conducting rigorous statistical inference. Furthermore, XLSTAT's support for linear regression analysis enables students to assess the strength and direction of relationships between continuous variables. By fitting regression models to their data and examining regression coefficients, students can evaluate hypotheses related to the predictive power of independent variables on the dependent variable, making informed decisions based on statistical evidence.

## Non-Parametric Tests

In contrast to parametric tests, non-parametric tests do not rely on strict assumptions about the underlying distribution of data. Instead, non-parametric tests offer robust alternatives for hypothesis testing in situations where parametric assumptions are violated, or data are non-normally distributed. XLSTAT provides a range of non-parametric tests that cater to such scenarios, ensuring the reliability and validity of statistical inference. The Wilcoxon signed-rank test, available in XLSTAT, is a non-parametric test commonly used to assess differences between paired samples or repeated measures. Whether students are evaluating the effectiveness of an intervention by comparing pre- and post-treatment outcomes or examining the impact of training on performance, the Wilcoxon signed-rank test offers a robust approach for hypothesis testing without relying on distributional assumptions.

Similarly, the Kruskal-Wallis test, also accessible through XLSTAT, serves as a non-parametric alternative to ANOVA when analyzing the differences between multiple independent groups. By rank-ordering the data and assessing the distribution of ranks across groups, students can conduct hypothesis tests with confidence, even in the presence of non-normal data distributions or unequal variances. By offering both parametric and non-parametric testing options, XLSTAT ensures that students have the flexibility and versatility to perform hypothesis testing robustly across diverse research scenarios. Whether the assumptions of parametric tests are met or not, XLSTAT equips students with the tools they need to conduct rigorous statistical inference, facilitating meaningful interpretation and decision-making based on empirical evidence.

## Multivariate Analysis

Multivariate analysis is a powerful statistical approach that involves analyzing datasets with multiple variables simultaneously. This technique allows students to explore complex relationships and patterns within their data, providing a more comprehensive understanding of the underlying structure. In academic and research settings, multivariate analysis is often employed to uncover hidden insights, identify trends, and make informed decisions based on data-driven evidence. XLSTAT, a leading statistical software package, offers a range of advanced multivariate analysis tools, empowering students to conduct in-depth analyses and extract valuable insights from their datasets.

## Principal Component Analysis

Principal Component Analysis (PCA) is a widely used dimensionality reduction technique in multivariate analysis. Its primary objective is to transform high-dimensional data into a lower-dimensional space while preserving the essential information contained in the original variables. PCA accomplishes this by identifying the principal components, which are orthogonal linear combinations of the original variables that capture the maximum variance in the data. By reducing the dimensionality of the dataset, PCA helps students explore the underlying structure of their data more effectively, revealing patterns and relationships that may not be apparent in the original space.

XLSTAT provides robust PCA tools that allow students to perform comprehensive analyses and visualize the results efficiently. With XLSTAT's PCA capabilities, students can conduct exploratory data analysis to gain insights into the relationships among variables, identify outliers, and detect multicollinearity. Additionally, XLSTAT offers various visualization options, such as scatter plots and biplots, which enable students to interpret the principal components and understand the structure of their data intuitively. By leveraging XLSTAT's PCA tools, students can streamline their data analysis workflow and extract meaningful insights from complex datasets with ease.

## Cluster Analysis

Cluster Analysis is a versatile technique used to identify natural groupings or clusters within a dataset based on the similarity of observations. It is commonly employed in various fields, including biology, marketing, and social sciences, to segment data into meaningful groups and uncover hidden patterns or structures. XLSTAT offers a range of clustering algorithms, including K-means clustering and hierarchical clustering, which enable students to explore the structure of their data and identify meaningful clusters efficiently. K-means clustering is a partitioning method that divides the dataset into a predetermined number of clusters based on the similarity of observations to cluster centroids. It is a popular clustering algorithm due to its simplicity and efficiency, making it well-suited for exploratory data analysis tasks. Hierarchical clustering, on the other hand, is a more flexible approach that organizes the data into a hierarchical structure of nested clusters.

XLSTAT's hierarchical clustering tools allow students to visualize dendrograms and explore different clustering solutions, enabling them to identify the optimal number of clusters for their dataset. By leveraging XLSTAT's cluster analysis capabilities, students can gain valuable insights into the structure of their data and identify meaningful groups or segments. Whether they are exploring customer segmentation in marketing research or identifying species clusters in biological datasets, XLSTAT provides students with the tools they need to conduct comprehensive cluster analyses and extract actionable insights from their data. With its user-friendly interface and robust analytical capabilities, XLSTAT empowers students to explore the complexities of multivariate data and unlock new opportunities for research and discovery.

In conclusion, XLSTAT stands out as a versatile statistical software package designed to cater to the diverse needs of students undertaking data analysis assignments. Its comprehensive array of features, spanning from seamless data import and efficient management to the generation of descriptive statistics and conducting hypothesis tests, empowers students to handle datasets with ease. By gaining proficiency in these top 10 XLSTAT features, students can elevate their analytical capabilities and achieve excellence in their academic endeavors. Whether navigating the software as a novice or an experienced user, XLSTAT serves as an invaluable ally for students aiming to fully leverage the richness of their data, thereby facilitating informed decision-making and insightful interpretations. In essence, XLSTAT emerges as an indispensable tool for students aspiring to unlock the full potential of their data and make meaningful contributions within their respective fields of study.

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## Correlation tests

- Cohen’s Kappa in Excel tutorial
- Pearson correlation coefficient in Excel
- Run Chi-square and Fisher’s exact tests in Excel
- RV coefficient test in Excel tutorial
- Biserial correlations tutorial in Excel
- Spearman correlation coefficient in Excel tutorial

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## Homogeneity tests for time series

Homogeneity tests for time series test allow to detect a change along a time series. Available in Excel using the XLSTAT statistical software.

## What are homogeneity tests for time series

Homogeneity tests enables you to determine if a series may be considered as homogeneous over time, or if there is a time at which a change occurs.

Homogeneity tests involve a large number of tests, XLSTAT offer four tests ( Pettitt , Buishand , SNHT , or von Neumann ), for which the null hypothesis is that a time series is homogenous between two given times.

The variety of the tests comes from the fact that there are many possible alternative hypotheses: changes in distribution, changes in average (one or more times) or presence of trend.

## Homogeneity tests p-value approximation

The tests presented in this tool correspond to the alternative hypothesis of a single shift. For all tests, XLSTAT provides p-values using Monte Carlo resamplings. Exact calculations are either impossible or too costly in computing time.

Note 1: If you have a clear idea of the time when the shift occurs, one can use the tests available in the parametric or nonparametric tests sections. For example, assuming that the variables follow normal distributions, one can use the test z (known variance) or the Student t test (estimated variance) to test the presence of a change at time t. If one believes that the variance changes, you can use a comparison test of variances (F-test in the normal case, for example, or Kolmogorov-Smirnov in a more general case).

Note 2: The tests presented below are sensitive to a trend (for example a linear trend). Before applying these tests, you need to be sure you want to identify a time at which there is a shift between two homogeneous series.

## Pettitt’s test

The Pettitt's test is a nonparametric test that requires no assumption about the distribution of data. The Pettitt's test is an adaptation of the tank-based Mann-Whitney test that allows identifying the time at which the shift occurs. In his article of 1979 Pettitt describes the null hypothesis as being that the T variables follow the same distribution F, and the alternative hypothesis as being that at a time t there is a change of distribution. Nevertheless, the Pettitt test does not detect a change in distribution if there is no change of location. For example, if before the time t, the variables follow a normal N(0,1) distribution and from time t a N (0,3) distribution, the Pettitt test will not detect a change in the same way a Mann-Whitney would not detect a change of position in such a case. In this case, one should use a Kolmogorov Smirnov based test or another method able to detect a change in another characteristic than the location. We thus reformulate the null and alternative hypotheses:

H0: The T variables follow one or more distributions that have the same location parameter.

Two-tailed test: Ha: There exists a time t from which the variables change of location parameter.

Left-tailed test: Ha: There exists a time t from which the variables location is reduced by D.

Left-tailed test: Ha: There exists a time t from which the variables location is augmented by D.

## Alexandersson’s SNHT test

The SNHT test (Standard Normal Homogeneity Test) was developed by Alexandersson (1986) to detect a change in a series of rainfall data. The test is applied to a series of ratios that compare the observations of a measuring station with the average of several stations. The ratios are then standardized. The series of Xi corresponds here to the standardized ratios. The null and alternative hypotheses are determined by:

H0: The T variables Xi follow a N(0,1) distribution.

Ha: Between times 1 and n the variables follow an N(µ1, 1) distribution, and between n+1 and T they follow an N(µ2,1) distribution.

## Buishand’s test

The Buishand’s test (1982) can be used on variables following any type of distribution. But its properties have been particularly studied for the normal case. In his article, Buishand focuses on the case of the two-tailed test, but for the Q statistic presented below the one-sided cases are also possible. Buishand has developed a second statistic R, for which only a bilateral hypothesis is possible.

In the case of the Q statistic, the null and alternative hypotheses are given by:

H0: The T variables follow one or more distributions that have the same mean. Two-tailed test: Ha: There exists a time t from which the variables change of mean.

Left-tailed test: Ha: There exists a time t from which the variables mean is reduced by D.

Left-tailed test: Ha: There exists a time t from which the variables mean is augmented by D.

## von Neumann’s ratio test

The von Neumann ratio test is very powerful at all times but does not allow detecting the time of the change.

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Hypothesis testing 41. Hypothesis testing. Compare two proportions in Excel tutorial. Parametric tests 13. Non parametric tests 18. Outliers tests 3. Correlation tests 6. Expert Software for Better Insights, Research, and Outcomes.

Six Steps for a Successful Hypothesis Test inside XLSTAT. There are several steps involved in conducting hypothesis testing, and while some corporations have unique stages for their individual test, you'll like go through these common steps: Null hypothesis: Also known as H0, this is the default assumption that you are making going into the test.

Parametric tests. One proportion test in Excel tutorial. Compare two proportions in Excel tutorial. Student's t test on two independent samples tutorial. Compare ≥ 2 samples described by several variables. Compare k proportions in Excel tutorial. One sample t-test or z-test in Excel tutorial. One sample variance test in Excel tutorial.

XLSTAT provides a high number of statistical tests. We have drawn the grid below to guide you through the choice of an appropriate statistical test according to your question, the type of your variables (i.e., categorical variables, binary, continuous) and the distribution of data. The guide proposes a formulation of the null hypothesis, as ...

Mann-Kendall trend test hypotheses. The null hypothesis H 0 for these tests is that there is no trend in the series. The three alternative hypotheses are that there is a negative, non-null, or positive trend. ... To calculate the p-value of this test, XLSTAT can calculate, as in the case of the Kendall tau test, an exact p-value if there are no ...

#statistics #walkthrough #xlstat #hypothesis #hypothesistesting #excel We demonstrate how to carry out a hypothesis test using XLSTAT, an Excel add-in. This ...

Three types of one-sample test are possible in XLSTAT depending on the alternative hypothesis chosen: For the two-tailed test, the null H0 and alternative Ha hypotheses are as follows: H0 : µ = µ0 Ha : µ ≠ µ0. In the left one-tailed test, the following hypotheses are used: H0 : µ = µ0 Ha : µ < µ0. In the right one-tailed test, the ...

Walkthrough on how to do a hypothesis test with a single mean using the XLSTAT add-on in Excel. This is a common statistical task for intermediate business a...

This video is a walkthrough of how to use XLSTAT to compare a proportion against a theoretical value. This is a common tasks in business analytics.@XLSTAT i...

Setting up a Student's t-test for comparing the mean of a sample to a value. Go over to the menu Parametric tests and select the option One-sample t-test and z-test . In the dialog box, select the data. Press the Range selector and select the column B that contains the "Math average". The data format is One sample per column.

The null hypothesis: There is no significant change of variable A related to variable B. Alternate hypothesis: There is a significant change of variable A related to variable B. ... With so many options, it might feel challenging to choose the correct statistical test for your analysis. That's where XLSTAT's MyAssistant tool can help ...

About. Here I demonstrate how i execute hypothesis testing, & build and test multiple regression profit model using Excel and XLSTAT ANOVA. I also demonstrate how to calculate key statistical values such as R square, adjusted R square, p-value, & F-value and ho they affect the interpretation and relevancy of modelling.

XLSTAT offers a comprehensive suite of tools for conducting hypothesis tests, empowering students to test various hypotheses and draw meaningful conclusions from their data. One of the key features of XLSTAT is its ability to perform t-tests, which are used to compare the means of two groups and determine whether there is a significant ...

Outliers tests 3. Outliers tests. Dixon test to detect outliers in Excel tutorial. Grubbs test to detect outliers in Excel tutorial. Cochran C test to detect outlying variances tutorial. Expert Software for Better Insights, Research, and Outcomes. English. Products. Webinars.

Table of contents. Step 1: State your null and alternate hypothesis. Step 2: Collect data. Step 3: Perform a statistical test. Step 4: Decide whether to reject or fail to reject your null hypothesis. Step 5: Present your findings. Other interesting articles. Frequently asked questions about hypothesis testing.

Thus we can use the z-test (for one or two proportions), chi-square test, the sign test or the McNemar test. XLSTAT can calculate the power or the number of observations necessary for these tests using either exact methods or approximations. When testing a hypothesis using a statistical test, there are several decisions to take:

Hypothesis testing is a fundamental concept in statistical analysis, serving as a cornerstone for making informed decisions and drawing conclusions about populations based on sample data. Within the realm of hypothesis testing, XLSTAT stands out as a robust tool that equips students with a diverse array of testing methodologies and analytical ...

Non parametric tests 18. Non parametric tests. Friedman-Rafsky test in Excel. Mann-Whitney test in Excel tutorial. Wilcoxon signed rank test on two paired samples in Excel tutorial. Friedman non parametric test in Excel tutorial. Kruskal-Wallis test in Excel tutorial. Kolmogorov-Smirnov test in Excel tutorial. Mood median test in Excel tutorial.

XLSTAT offers a test to compare correlations. XLSTAT can calculate the power or the number of observations necessary for this test. When testing a hypothesis using a statistical test, there are several decisions to take: The null hypothesis H0 and the alternative hypothesis Ha.

The alternative hypothesis and null hypothesis are types of conjectures used in statistical tests, which are formal methods of reaching conclusions or making judgments on the basis of data. In statistical hypothesis testing, the null hypothesis and alternative hypothesis are two mutually exclusive statements. "The statement being tested in a test of statistical significance is called the null ...

XLSTAT-Power estimates the power or calculates the necessary number of observations associated with variations of R ² in the framework of a linear regression. When testing a hypothesis using a statistical test, there are several decisions to take: The null hypothesis H0 and the alternative hypothesis Ha. The statistical test to use.

Correlation tests. Cohen's Kappa in Excel tutorial. Pearson correlation coefficient in Excel. Run Chi-square and Fisher's exact tests in Excel. RV coefficient test in Excel tutorial. Biserial correlations tutorial in Excel. Spearman correlation coefficient in Excel tutorial. Expert Software for Better Insights, Research, and Outcomes.

Homogeneity tests involve a large number of tests, XLSTAT offer four tests ( Pettitt, Buishand, SNHT, or von Neumann ), for which the null hypothesis is that a time series is homogenous between two given times. The variety of the tests comes from the fact that there are many possible alternative hypotheses: changes in distribution, changes in ...