Exercise

# Visualizing the P-Value

In this exercise, you will visualize the p-value, the chance that the effect (or "speed") we estimated, was the result of random variation in the sample. Your goal is to visualize this as the fraction of points in the shuffled test statistic distribution that fall to the right of the mean of the test statistic ("effect size") computed from the unshuffled samples.

To get you started, we've preloaded the `group_duration_short`

and `group_duration_long`

and functions `compute_test_statistic()`

, `shuffle_and_split()`

, and `plot_test_statistic_effect()`

Instructions

**100 XP**

- Use
`compute_test_statistic()`

to get`test_statistic_unshuffled`

from the`group_duration_short`

and`group_duration_long`

; then use`np.mean()`

to compute effect size. - Use
`shuffle_and_split()`

to create`shuffle_half1`

and`shuffle_half2`

, and use`compute_test_statistic()`

to compute the`test_statistic_shuffled`

. - Create a boolean mask
`condition`

`test_statistic_shuffled`

values are greater than or equal to`effect_size`

, then use this mask to compute the`p_value`

. - Print the
`p_value`

and plot both test statistics using`plot_test_statistic_effect()`

.