## 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?

Course Content

Learning Statistics with Python

# Learning Statistics with Python

2. Mean, Median and Mode with Python

4. Covariance vs Correlation

## 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?