Ran through the usual list: normality, independence, roughly equal variances for the two-sample version.
Start by listing the core assumptions of the t-test, then explain why each matters and how violations can be detected and addressed. Emphasize practical implications in A/B testing at Amazon, showing you understand both theory and application.
Pro tip: Mention that with large sample sizes (common in Amazon experiments), the t-test is robust to some violations, but always check for practical significance and consider alternatives like bootstrapping or non-parametric tests when assumptions are severely violated.
List the main assumptions: independence, normality (or large sample), homogeneity of variance, and for paired t-tests, paired differences are normal.
Briefly describe what each assumption means and why it is important for the validity of the t-test.
Explain how to check each assumption (e.g., plots, Levene's test) and what happens if violated (e.g., inflated Type I error).
Suggest solutions: transformations, Welch's t-test, non-parametric tests, or bootstrapping. Mention robustness with large samples.
Connect to A/B testing: large samples often make t-test robust, but always consider practical significance and business impact.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying that the t-test is a parametric test with specific assumptions (normality, independence, homogeneity of variance, continuous data). Then systematically walk through scenarios where these assumptions are violated or the data type is different, explaining which alternative test to use and why. Finally, tie it back to practical A/B testing contexts at Amazon, emphasizing the importance of choosing the right test to avoid false positives/negatives.
Pro tip: Mention that in large-scale A/B tests, the t-test is often robust due to the Central Limit Theorem, but you must still check for ratio metrics or skewed distributions where alternatives like bootstrapping or Mann-Whitney U are more appropriate. This shows you understand both theory and real-world application.
Briefly list the key assumptions: independence, normality (or large sample), homogeneity of variance, and continuous data. This sets the stage for when to use alternatives.
Discuss common violations: non-normal data (especially small samples), ordinal data, unequal variances, paired vs. independent samples, and multiple groups. Also consider non-continuous outcomes like counts or proportions.
For each violation, name the appropriate test: Mann-Whitney U or Wilcoxon for non-normal/ordinal, Welch's t-test for unequal variances, paired t-test or Wilcoxon signed-rank for paired data, ANOVA or Kruskal-Wallis for multiple groups, chi-square for categorical, and bootstrapping for complex metrics.
Explain how these alternatives apply in A/B testing: e.g., using Mann-Whitney for skewed revenue data, bootstrapping for ratio metrics like CTR, or chi-square for conversion rates. Highlight the impact on decision-making.
Summarize that the choice depends on data characteristics and business context, and emphasize the importance of checking assumptions before defaulting to a t-test.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.