This one had a lot of layers and I underestimated how long it would take to even get to the SQL part.
Start by clarifying the business question and defining the target metric (e.g., upload rate per member per week). Then design an observational analysis that controls for confounders like membership size and tenure, using regression or matching to isolate the US vs. non-US effect. Finally, validate with robustness checks and consider an experiment if feasible.
Pro tip: Always check for data quality issues like bot activity or duplicate accounts before diving into analysis; these can skew upload rates and lead to false conclusions.
Clarify what 'upload rate' means (e.g., videos uploaded per member per week) and state the null and alternative hypotheses. Ensure the metric is comparable across regions.
List potential confounders such as membership size (e.g., connections), tenure, activity level, and device type. Plan to control for them using regression, stratification, or matching.
Select an appropriate statistical method (e.g., multiple linear regression, Poisson regression for count data, or propensity score matching) to estimate the effect of US vs. non-US while adjusting for confounders.
Execute the analysis, check assumptions, and interpret the coefficient for US membership. Assess statistical significance and effect size, and consider practical significance.
Perform robustness checks (e.g., sensitivity analysis, alternative model specifications) and communicate findings with clear caveats about causality.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.