← Attentive Interview Insights
My first instinct was to say 'two out of thirty is actually pretty good' and I almost stopped there, which would've been embarrassing.
First, acknowledge that with 30 independent tests at α=0.05, we expect about 1.5 false positives by chance alone, so the observed results (2 significant lifts, 1 significant drop) are roughly consistent with noise. Then, discuss methods to control for multiple testing, such as Bonferroni correction or false discovery rate (FDR), and emphasize the need to consider practical significance and business context before drawing conclusions.
Pro tip: Don't just focus on statistical significance; highlight the importance of effect sizes and confidence intervals to assess practical relevance, and suggest pre-registering hypotheses or using hierarchical models to share information across brands.
Calculate the expected number of false positives under the null hypothesis (30 * 0.05 = 1.5) and note that observing 3 significant results is not surprising.
Discuss correction methods like Bonferroni (α = 0.05/30 ≈ 0.0017) or Benjamini-Hochberg FDR, and note that under these corrections, none of the results would likely remain significant.
Recognize that brands may not be independent; use hierarchical models or meta-analysis to borrow strength across brands and estimate overall effect.
Even if statistically significant, assess whether the lift/drop is meaningful for the business, and consider the cost of false positives vs. false negatives.
Suggest replicating the experiment, increasing sample size, or running a follow-up test on the brands that showed signals to confirm findings.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.