Navigating the Maze of Multiple Hypotheses Testing—Part 2: Practical Implementation

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In this article, we will explore practical implementation with Python code and interpretation of the results.
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In this article, we will explore practical implementation with Python code and interpretation of the results. The Bonferroni correction makes the p-values higher to control for the increased risk of Type I errors (false positives) that come with multiple testing. In this case, the first (`True`) and last (` true`) hypotheses are rejected.