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How to use Python to Perform a Paired Sample T-test

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dependent sample t-test in Python

In this Python data analysis tutorial, you will learn how to perform a paired sample t-test in Python. First, you will learn about this type of t-test (e.g. when to use it, the assumptions of the test). Second, you will learn how to check whether your data follow the assumptions and what you can do if your data violates some of the assumptions. 

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How to Perform Mann-Whitney U Test in Python with Scipy and Pingouin

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In this data analysis tutorial, you will learn how to carry out a Mann-Whitney U test in Python with the packages SciPy and Pingouin. This test is also known as Mann–Whitney–Wilcoxon (MWW), Wilcoxon rank-sum test, or Wilcoxon–Mann–Whitney test and is a non-parametric hypothesis test.

Outline of the Post

This tutorial will teach you when and how to use this non-parametric test.  After that, we will see an example of a situation when the Mann-Whitney U test can be used. The example is followed by how to install the needed package (i.e., SciPy) as well as a package that makes importing data easy and that we can quickly visualize the data to support the interpretation of the results. In the following section, you will learn the two steps to carry out the Mann-Whitney-Wilcoxon test in Python. Note we will also look at another package, Pingouin, that enables us to carry out statistical tests with Python. Finally, we will learn how to interpret the results and visualize data to support our interpretation.

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How to Perform a Two-Sample T-test with Python: 3 Different Methods

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two sample t-test Python

In this Python data analysis tutorial, you will learn how to perform a two-sample t-test with Python. First, you will learn about the t-test including the assumptions of the statistical test. Following this, you will learn how to check whether your data follow the assumptions. 

After this, you will learn how to perform an two sample t-test using the following Python packages:

  • Scipy (scipy.stats.ttest_ind)
  • Pingouin (pingouin.ttest)
  • Statsmodels (statsmodels.stats.weightstats.ttest_ind)
  • Interpret and report the two-sample t-test
    • Including effect sizes

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