Start by clarifying the goal: are you testing for any distributional difference or specific shifts? Then systematically address continuous and categorical variables with appropriate visual and statistical methods, considering assumptions, effect sizes, and multiple testing. Finally, discuss how unequal sample sizes affect test choice and interpretation, emphasizing practical significance over p-values alone.
Pro tip: Always pair statistical tests with effect size measures and visualizations; a tiny p-value with a trivial effect size is common with large samples and can mislead stakeholders. Also, consider bootstrapping or permutation tests when assumptions are dubious, especially with unequal sample sizes.
Determine whether the goal is to detect any difference or a specific shift, and separate variables into continuous and categorical. This guides the choice of tests and diagnostics.
Use histograms, density plots, boxplots, Q-Q plots for continuous variables; bar charts and mosaic plots for categorical. These reveal distribution shape, outliers, and potential differences before formal testing.
For continuous: if normality and equal variance hold, use t-test (mean) or F-test (variance); otherwise use nonparametric tests like Mann-Whitney U (median/shift), Kolmogorov-Smirnov (any difference), or Anderson-Darling. For categorical: use chi-square or Fisher's exact test. Adjust for multiple comparisons (e.g., Bonferroni, FDR).
Compute Cohen's d, Cliff's delta, Cramér's V, or overlap measures. Interpret in context: a statistically significant difference may be practically irrelevant if effect size is small.
With very unequal sizes, parametric tests may be sensitive to variance differences; use Welch's t-test or nonparametric alternatives. Consider permutation tests or bootstrapping to assess differences in distributions without strict assumptions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked a little on importance sampling, which is embarrassing given the autonomous driving context where it's actually super relevant for rare event evaluation.
Start by briefly defining each sampling method and its typical use case, then explain when each is appropriate based on data characteristics and goals. For product evaluation, recommend stratified or weighted sampling to ensure representative and unbiased results, and justify your choice with practical considerations like cost, accuracy, and feasibility.
Pro tip: Emphasize that the choice of sampling method depends on the specific product metrics and potential biases; for A/B testing, stratified sampling by key user segments often yields more precise treatment effect estimates than simple random sampling.
Clearly explain simple random, stratified, cluster, systematic, and weighted/importance sampling, including how each is implemented.
Describe scenarios where each method is most appropriate, considering factors like population homogeneity, cost, and need for representation.
Discuss pros and cons such as bias, variance, cost, and practicality for each method in the context of data science.
State your preferred method(s) for product evaluation, typically stratified or weighted sampling, and justify why they are suitable for A/B testing and experimentation.
Tie your recommendation to how it improves decision-making, reduces bias, and aligns with company goals like accurate measurement of product changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.