Course Content

# Learning Statistics with Python

2. Mean, Median and Mode with Python

3. Variance and Standard Deviation

4. Covariance vs Correlation

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 compared groups should be approximately the same.**Normality**. Both samples should roughly follow a Normal distribution.**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.

Everything was clear?

Section 6. Chapter 5