Exercise

# Calculating the p-values

In the video, you learned that a p-value measures the degree of disagreement between the data and the null hypothesis. Here, you will calculate the p-value for the original discrimination dataset as well as the small and big versions, `disc_small`

and `disc_big`

.

The original differences in proportions are available in your workspace, `diff_orig`

, `diff_orig_small`

, and `diff_orig_big`

, as are the permuted datasets, `disc_perm`

, `disc_perm_small`

, and `disc_perm_big`

.

Recall that you're only interested in the one-sided hypothesis test here. That is, you're trying to answer the question, "Are men more likely to be promoted than women?"

Instructions

**100 XP**

`visualize()`

and`get_p_value()`

using the built in`infer`

functions. Remember that the null statistics are below the original difference, so the p-value (which represents how often a null value is more*extreme*) is calculated by counting the number of null values which are`greater`

than the original difference.- Repeat for the small dataset,
`disc_perm_small`

, which has observed difference`diff_orig_small`

. - Repeat for the big dataset,
`disc_perm_big`

, which has observed difference`diff_orig_big`

. - You can test your knowledge by trying out:
`direction = "greater"`

,`direction = "two_sided"`

, and`direction = "less"`

before submitting your answer.