Course Content

Learning Statistics with Python

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:

**Homogeneity of variance**. The variances of the two groups being compared must be roughly equal.**Normality**. Both samples should be approximately Normally distributed.**Independence**The samples are independent. This means the values in one group cannot be influenced by the values in the other group.

The t-test may not provide accurate results if the 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
**paired t-test**. A paired t-test will be discussed in a later chapter.

Section 6.

Chapter 5