15. Quiz: Analyzing Multiple Metrics

Bonferroni Correction

If you remember from the previous lesson, the Bonferroni Correction is one way we could handle experiments with multiple tests, or metrics in this case. To compute the new bonferroni correct alpha value, we need to divide the original alpha value by the number of tests.

If our original alpha value was 0.05, what would be our new, Bonferroni corrected alpha value, considering we had four tests?
SOLUTION: 0.0125

What results are still statistically significant?

Let's see which of our metrics produced statistically significant differences based on this new Bonferroni corrected alpha value. Here are the p-values computed for the four metrics in this experiment. (These are the values you should've gotten with a random seed of 42.)

  1. Enrollment Rate: 0.0188
  2. Average Reading Duration: 0
  3. Average Classroom Time: 0.0384
  4. Completion Rate: 0.0846

With the Bonferroni corrected alpha value, which of the following metrics produced statistically significant results? Select all that apply.
SOLUTION:
  • Average reading duration

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