I started with the visual layer, retention curves overlaid per cohort, average posts per user over time, that kind of thing.
Start by defining cohorts and the posting metrics you computed, then outline a layered analysis: descriptive statistics and visualizations to spot patterns, followed by appropriate statistical tests to confirm differences. Conclude by interpreting results in the context of TikTok's product and business goals, considering practical significance and potential confounders.
Pro tip: Emphasize that statistical significance alone isn't enough—quantify effect sizes and relate them to business impact, such as changes in user engagement or content diversity, to show you think like a TikTok data scientist.
Clarify how cohorts are defined (e.g., by signup date, region, user type) and which posting metrics you computed (e.g., posts per user, posting frequency, content type distribution). Ensure metrics are comparable across cohorts.
Compute summary statistics (mean, median, variance) per cohort and create visualizations like box plots, bar charts with error bars, or time series to visually inspect differences and trends.
Choose tests based on data type and assumptions: e.g., ANOVA or Kruskal-Wallis for multiple cohorts, t-tests or Mann-Whitney U for pairwise comparisons, chi-square for categorical metrics. Check assumptions and adjust for multiple comparisons.
Calculate effect sizes (e.g., Cohen's d, eta-squared) and confidence intervals. Consider potential confounders (e.g., seasonality, platform changes) and whether differences are meaningful for TikTok's product.
Integrate statistical findings with business context to determine if posting patterns truly differ. Recommend next steps, such as deeper dives or experiments, if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.