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  3. Introduction to Linear Modeling in Python

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().