How to report test results

Designing and executing an A/B test involves meticulous planning and analysis, but effectively communicating the results is crucial for making a meaningful impact. Common errors in reporting include overstating certainty by neglecting the inherent probabilistic nature of statistics, confusing test settings with p-values, and misinterpreting p-values and confidence intervals. Analysts often mistakenly generalize sample results to the population without acknowledging limitations, leading to overly definitive conclusions. To convey uncertainty accurately, cautious language and statistical methods like confidence intervals should be used. It's important to distinguish between alpha and p-values, as the former is a predefined parameter indicating error rates, while the latter reflects the probability of obtaining observed results under the null hypothesis. Confidence intervals should be attributed to the process rather than the true value, and considerations of external validity, such as timing and user profile, should be included to avoid unwarranted generalizations. A well-structured report with key findings, visualizations, and context can effectively convey the test's results and insights for future directions.