Course Content

Learning Statistics with Python

Performing a t-test in Python

To perform a t-test in Python, you only need to choose the alternative hypothesis and whether variances are homogeneous(roughly equal).

The `ttest_ind()`

function from `scipy.stats`

takes care of everything else. Here is its syntax:

Parameters:

`a`

— first sample;`b`

— second sample;`equal_var`

— set`True`

if variances are roughly equal and`False`

if they aren't;`alternative`

— type of alternative hypothesis:`'two-sided'`

— means are not equal;`'less'`

— the first mean is less than the second;`'greater'`

— the first mean is greater than the second.

Returns:

`statistic`

— the value of**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 an example of applying t-test to our heights dataset:

Section 6.

Chapter 6