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

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This story was originally published on HackerNoon at: https://hackernoon.com/navigating-the-maze-of-multiple-hypotheses-testingpart-2-practical-implementation.
In this article, we will explore practical implementation with Python code and interpretation of the results.
Check more stories related to science at: https://hackernoon.com/c/science. You can also check exclusive content about #statistics, #python, #data-analysis, #bonferroni-correction, #hypothesis-testing, #statistical-significance, #p-values, #data-interpretation, and more.
This story was written by: @vabars. Learn more about this writer by checking @vabars's about page, and for more stories, please visit hackernoon.com.
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.

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

Science Tech Brief By HackerNoon

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