What is the multiple comparisons problem in UX research?
This tests whether you know running many tests inflates false positives. Strong answers define family-wise error, give a UX example like comparing twenty metrics in one A/B test, and name a correction like Bonferroni.
This tests your grasp of how repeated statistical testing inflates Type I error in UX work. A strong answer defines the problem as the rising chance of at least one false positive when many hypotheses are tested on the same data. It then gives a concrete UX scenario, such as an A/B test tracking twenty metrics or comparing five variants, and names a correction like Bonferroni or Benjamini-Hochberg with a brief note on when each fits.
Read the original → Wikipedia: Multiple comparisons problem
- #ux research
- #statistics
- #ab testing
- #type i error
- #data literacy
Get five bites like this every day.
Tezvyn delivers a daily feed of 60-second tech bites with quizzes to lock in what you learn.