The twist is that all three are negative, so the instinct is to just say 'none of them' and move on.
Interpret each confidence interval in terms of statistical significance and practical impact, then weigh the trade-offs between effect size, uncertainty, and business risk. Conclude that none should be launched as-is, but recommend further investigation for treatments with promising signals or high uncertainty.
Pro tip: Emphasize that statistical significance alone isn't enough; consider the cost of a false negative (missing a good feature) versus a false positive (launching a harmful change), and align with product goals.
For each treatment, determine whether the interval excludes zero (statistical significance) and describe the range of plausible effect sizes. Note that all intervals are entirely negative, indicating significant negative effects.
Evaluate the magnitude of the effect and the width of the interval. A wider interval (e.g., Treatment C) indicates more uncertainty, which may warrant further data collection before making a decision.
Rank treatments by effect size and confidence. Treatment B has the most negative and precise estimate, making it the worst. Treatment A has the smallest negative effect, but still significant. Treatment C is highly uncertain.
Conclude that none should be launched because all show significant negative effects. However, if forced to choose, Treatment A is the least harmful, but it's still negative. Recommend not launching any and possibly investigating why all treatments hurt engagement.
Propose additional experiments or analyses to understand the negative effects, such as segment analysis, longer run time, or qualitative research. Consider whether the metric is appropriate or if there are novelty effects.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.