Articles by Emily Robinson
Guidelines For Ab TestingEmily provides 12 guidelines for effective A/B testing, including having one key metric, doing a power calculation to determine test length, not stopping early for significance, paying attention to confidence intervals over p-values, limiting variants tested, avoiding segment-level differences, and checking for bucketing skew. She emphasizes the importance of involving a data scientist from the start and testing smaller changes incrementally. An interesting point is that revenue is generally a poor choice for the key metric due to its skewed distribution, and proportion metrics are preferable.