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5 articles tagged #Analytics.
P.01The direct answer: calculate your required sample size before you start, based on your baseline conversion rate and the smallest effect worth detecting, then run until you hit it. Stopping early on a good-looking p-value is how most A/B test results turn out to be noise.
P.02ClickHouse is a columnar database designed for analytical workloads. It answers queries over billions of rows in seconds that would take minutes in Postgres. Here's what application developers need to know.
P.03dbt turns SQL SELECT statements into a tested, documented, version-controlled data pipeline. Here's how it works and when you should add it to your data stack.
P.04Most teams pick an analytics tool based on a free trial and then live with that choice for years. Here's an honest comparison of PostHog, Mixpanel, and Amplitude so you pick the right one before the data gets messy.
P.05Running totals, rankings, moving averages, lag and lead comparisons: window functions handle the analytics queries that would otherwise require application-side loops or multiple round-trips. Here's how they work.