I knew the concepts but fumbled a bit explaining how to actually distinguish between the two in practice.
Start by defining novelty and primacy effects in the context of A/B testing, then explain how to detect them using time-series analysis of the treatment effect. Emphasize segmenting users by exposure time or experience level and applying statistical tests to identify significant changes in effect size over time.
Pro tip: When detecting novelty effects, focus on the first few days of the experiment and compare early vs. later periods; for primacy effects, look at whether the effect grows over time. Use cumulative metrics and consider both statistical and practical significance.
Clarify what novelty and primacy effects are: novelty effect is a temporary uplift due to newness, while primacy effect is a temporary dip due to unfamiliarity. Both manifest as changes in treatment effect over time.
Divide the experiment duration into periods (e.g., days since first exposure) and compute the treatment effect for each period. Plot the effect over time to visually inspect trends.
Apply statistical tests to compare early vs. later periods, such as a t-test on the difference in differences or a regression with time interaction. Check for significant changes in effect size.
Analyze subgroups based on user tenure or prior exposure to similar features. New users may show novelty effects, while experienced users may show primacy effects.
If effects are detected, consider extending the experiment, excluding early data, or using a holdout to measure long-term impact. Communicate findings to stakeholders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.