Course Content

Learning Statistics with Python

## Learning Statistics with Python

2. Mean, Median and Mode with Python

4. Covariance vs Correlation

# Paired t-test

The following function conducts a paired t-test:

This process resembles the one used for independent samples, but here we do not need to check the homogeneity of variance. The paired t-test explicitly **does not** assume that variances are equal.

Keep in mind that for a paired t-test, it's crucial that the **sample sizes are equal**.

With this information in mind, you can proceed to the task of conducting a paired t-test.

Here, you have data regarding the number of downloads for a particular app. Take a look at the samples: the mean values are nearly identical.

Task

We establish the hypotheses:

- H₀: The mean number of downloads before and after the changes is the same;
- Hₐ: The mean number of downloads is greater after the modifications.

Conduct a paired t-test with this alternative hypothesis, using `before`

and `after`

as the samples.

Everything was clear?

# Paired t-test

The following function conducts a paired t-test:

This process resembles the one used for independent samples, but here we do not need to check the homogeneity of variance. The paired t-test explicitly **does not** assume that variances are equal.

Keep in mind that for a paired t-test, it's crucial that the **sample sizes are equal**.

With this information in mind, you can proceed to the task of conducting a paired t-test.

Here, you have data regarding the number of downloads for a particular app. Take a look at the samples: the mean values are nearly identical.

Task

We establish the hypotheses:

- H₀: The mean number of downloads before and after the changes is the same;
- Hₐ: The mean number of downloads is greater after the modifications.

Conduct a paired t-test with this alternative hypothesis, using `before`

and `after`

as the samples.

Everything was clear?