First, clarify the table schema and define 'older' vs 'newer' users based on account age or tenure. Then, choose a metric like the odds ratio or risk ratio of setting shop invisible, computed with SQL using conditional aggregation and window functions if needed. Finally, justify why this metric is appropriate for comparing two groups in a product analytics context.
Pro tip: Mention that you would check for confounding variables like user activity level or shop type, and consider using a logistic regression model for a more robust comparison if the data allows.
Inspect the table schema to identify columns for user ID, action timestamp, action type (visible/invisible), and user signup date. Define 'older' and 'newer' users based on a threshold of account age (e.g., median tenure).
Select a metric that compares the likelihood of the action between groups, such as the odds ratio or risk ratio. Consider if you need to account for time or repeated actions per user.
Use conditional aggregation to compute the number of users who set shop invisible and total users in each group. Then calculate the metric, possibly using subqueries or CTEs for clarity.
Explain why the chosen metric is suitable: e.g., odds ratio is interpretable, handles unbalanced group sizes, and is standard in product analytics for comparing binary outcomes.
Discuss handling users with multiple actions (e.g., take the latest action or first action), missing data, and potential confounders. Suggest validating results with a statistical test or model.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.