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Impara t-test Assumptions | Statistical Testing
Learning Statistics with Python

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t-test Assumptions

The main idea behind the t-test is that it follows the t-distribution. For it to be true, a few important assumptions are made:

  1. Homogeneity of Variance. The variances of the two compared groups should be approximately the same;

  2. Normality. Both samples should roughly follow a Normal distribution;

  3. Independence. The samples need to be independent, implying that the values in one group shouldn't be influenced by those in the other group.

It's important to note that the t-test may yield inaccurate results if these assumptions are not met.

There are different types of t-tests that handle violations of some of the assumptions:

  • If the variances are different, you can run Welch's t-test. Its idea is the same. The only thing that differs is the degrees of freedom. Performing Welch's t-test instead of the ordinary t-test in Python is as easy as setting equal_var=False;
  • If samples are not independent(for example, if you want to compare the means of the same group at different time periods), you can run a paired t-test. A paired t-test will be discussed in a later chapter.
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Select the appropriate type of t-test for each case:

Normality, Homogeneity but no Independence —
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Normality, Homogeneity, Independence —
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Normality, Independence but no Homogeneity —
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Click or drag`n`drop items and fill in the blanks

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Paired t-test
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Regular t-test
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Welch's t-test

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Sezione 6. Capitolo 5
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