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Bonferroni correction

Let's implement multiple hypothesis tests using the Bonferroni correction approach that we discussed in the slides. You'll use the imported multipletests() function in order to achieve this.

Use a single-test significance level of .05 and observe how the Bonferroni correction affects our sample list of p-values already created.

Diese Übung ist Teil des Kurses

Practicing Statistics Interview Questions in Python

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Anleitung zur Übung

  • Compute a list of the Bonferroni adjusted p-values using the imported multipletests() function.
  • Print the results of the multiple hypothesis tests returned in index 0 of your p_adjusted variable.
  • Print the p-values themselves returned in index 1 of your p_adjusted variable.

Interaktive Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

from statsmodels.sandbox.stats.multicomp import multipletests
pvals = [.01, .05, .10, .50, .99]

# Create a list of the adjusted p-values
p_adjusted = multipletests(____, alpha=____, method='bonferroni')

# Print the resulting conclusions
print(____)

# Print the adjusted p-values themselves 
print(____)
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