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 groups being compared must be roughly equal.
2. Normality. Both samples should be approximately Normally distributed.
3. 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.