Understand averages, distributions, correlation, and significance well enough to never be fooled by a number.
Data & AnalyticsPDF · 10 pages· v1.0
4.5You do not need a statistics degree to work with data, but you do need enough to avoid being fooled, by your own numbers and by other people's charts. This free guide gives you the practical statistical literacy that matters in analytics, explained in plain language with everyday examples. It is for analysts, marketers, product people, and the data-curious who want to reason soundly about numbers without wading through formulas and proofs. If you can read a chart, you can read this. You will understand why the mean lies when data is skewed (and when to use the median instead), what the spread of data tells you that the average hides, why "correlation is not causation" is more than a slogan, the base rate fallacy that makes scary-sounding statistics misleading, what a percentage change really means versus percentage points, and what statistical significance does and does not prove. The guide is built around the traps that fool smart people: averaging averages, ignoring sample size, confusing relative and absolute risk, reading patterns into randomness, and survivorship bias. Each concept comes with a concrete "how this fools people" example. The outcome: numerical street smarts. You will catch misleading statistics, ask the right questions of any analysis, and reason more soundly about your own. This is a free pillar of our Data & Analytics line, pair it with the A/B Testing guide for the full picture.
Yes, $0. It is a free pillar of our Data & Analytics line, complete on its own and a natural companion to the paid A/B Testing and metrics guides.
No. There are no proofs and almost no formulas. The few that appear (like how to read a percentage change) are arithmetic you already know. The focus is reasoning, not calculation.
Yes. The traps it covers, base rates, relative vs absolute risk, correlation/causation, survivorship, are exactly the ones that make headlines and charts misleading. You will spot them everywhere.
At a conceptual level, what it means and what it does not prove, which is what most people get wrong. For running tests in practice, pair it with our A/B Testing Basics guide.
Read the full refund policy and trust & safety terms.