The more the merrier? The problem of multiple comparisons in A/B Testing

Multiple comparisons pose a significant challenge in statistical analysis, particularly in A/B testing, as they increase the likelihood of false positives, or Type I errors, when analyzing multiple key performance indicators (KPIs) or conducting multiple tests. This issue arises when multiple data analyses or hypotheses tests are conducted concurrently, leading to an elevated chance of incorrectly rejecting a true null hypothesis, akin to a shooter missing more shots as the number of attempts increases. Common scenarios in A/B testing that exacerbate this problem include frequent data peeking, segment analysis, tracking multiple KPIs, and running A/B/C/n tests. To mitigate these risks, statisticians employ various correction methods, such as the Bonferroni Correction, Dunnett’s Test, Benjamini-Hochberg Procedure, and Sequential Testing, each with its strengths and limitations. These techniques adjust the criteria for rejecting null hypotheses to maintain the desired error rate while striving to preserve the statistical power needed to detect true effects, thereby facilitating more reliable and informed data-driven decisions.