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.

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Practicing Statistics Interview Questions in Python

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Istruzioni dell'esercizio

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.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

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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Questo esercizio fa parte del corso

Practicing Statistics Interview Questions in Python

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Prepare to dive deeper into crucial concepts regarding experiments and testing by reviewing confidence intervals, hypothesis testing, multiple tests, and the role that power and sample size play. We'll also discuss types of errors, and what they mean in practice.

Exercise 1: Confidence intervals Exercise 2: Confidence interval by hand Exercise 3: Applying confidence intervals Exercise 4: Hypothesis testing Exercise 5: One tailed z-test Exercise 6: Two tailed t-test Exercise 7: Power and sample size Exercise 8: Effect on type II error Exercise 9: Calculating sample size Exercise 10: Visualizing the relationship Exercise 11: Multiple testing Exercise 12: Calculating error rates Exercise 13: Bonferroni correction

Esercizio in corso

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