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Bonferonni-Holm correction

You've seen that comparing many different datasets, even randomly generated ones, can result in "statistically significant relationships" that are anything but! One way around this is to apply a correction to the alpha of your confidence level. In this exercise you'll explore why you should apply this correction and how to do so.

The 1000 p-values you calculated in the previous exercise have been loaded for you in a NumPy array p_values, as has the package NumPy as np.

This exercise is part of the course

Foundations of Inference in Python

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Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Compute the Bonferonni-corrected alpha
bonf_alpha = ____

# Check how many p-values were significant at this level
____
Edit and Run Code