  Course Content

# Learning Statistics with Python

Learning Statistics with Python

##   Performing a t-test in Python

To conduct a t-test in Python, all you have to do is specify the alternative hypothesis and indicate whether variances are roughly equal (homogeneous).

The `ttest_ind()` function within `scipy.stats` handles the rest. Below is the syntax:

Parameters:

• `a` — the first sample.
• `b` — the second sample.
• `equal_var` — set to `True` if variances are approximately equal, and `False` if they are not.
• `alternative` — the type of alternative hypothesis:
• `'two-sided'` — indicates that the means are not equal.
• `'less'` — implies that the first mean is less than the second.
• `'greater'` — implies that the first mean is greater than the second.

Return values:

• `statistic` — the value of the t statistic.
• `pvalue` — the p-value.

We are interested in the `pvalue`. If it is lower than α(usually 0.05), then the t statistic is in the critical region, so we should accept the alternative hypothesis. And if `pvalue` is greater than α — we accept the null hypothesis that means are equal.

Here is an example of applying the t-test to our heights dataset:  Everything was clear?

Section 6. Chapter 6