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Experimental design, data analysis, and statistical tooling for modern teams. No hype, just the math.

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StatsTest
Time SeriesJan 29New

Comparing Pre/Post Periods: Difference-in-Differences for Product

How to use difference-in-differences to measure product impact by comparing treatment and control groups across pre and post periods.

statstest_flowTime SeriesSupporting
StatsTest
Time SeriesJan 29New

Granger Causality: Does Feature Usage Actually Drive Retention?

How to use Granger causality to test whether feature usage predicts retention, and why correlation over time is not causation.

statstest_flowTime SeriesSupporting
StatsTest
Time SeriesJan 29New

Interrupted Time Series: Measuring Impact Without a Control Group

How to use interrupted time series analysis to measure causal impact of launches, policy changes, and events without a control group.

statstest_flowTime SeriesSupporting