Statistics
10 articles in this topic.
Simpson's paradox is already in your dashboard
The new checkout converts better on desktop and better on mobile, and worse overall. Both statements are arithmetically correct, and one of them is about to be presented to your leadership team.
Peeking makes A/B tests lie; CUPED reduces the traffic cost
Peeking can raise the false-positive rate from 5% to 26.1% in a specific small A/B-test simulation. Predeclared endpoints and sequential inference keep monitoring honest; CUPED reduces variance and traffic cost without fixing peeking.
A/B testing is a sample-size problem wearing a statistics costume
Most A/B-test failures aren't bad statistics — they're underpowered tests that never had a chance of seeing the effect they were designed to find.
Why the average customer does not exist
The mean is a liar on skewed data, and almost all business data is skewed — here is how to stop building products for a customer who never existed.
Bayes' theorem is just updating beliefs with evidence
A 99%-accurate test sounds iron-clad until you realize that, for rare diseases, a positive result is probably wrong — and Bayes explains exactly why.
Correlation isn't causation — but here's what it actually is
Pearson's r is a precise, fragile number: it measures linear co-movement on a scale from -1 to 1, and almost everything interesting about causality lies in what it cannot see.
Expected value: how professionals make peace with uncertainty
The number you should optimize for isn't the most likely outcome — it's the probability-weighted average of all outcomes, and ignoring that difference is the hidden tax on every bad business decision.
p-values are not the probability you are right
A p-value of 0.04 does not mean there is a 96 percent chance your variant wins — it means something far stranger, and knowing the difference is what separates analysts who ship good decisions from analysts who ship confident noise.
Standard deviation, explained without the formula
Two archers with identical averages and completely different groupings reveal everything you need to know about spread, punishment for outliers, and why the formula does what it does.
Why everything looks normal: the central limit theorem
The bell curve colonizes measurement not because the world is Gaussian but because averaging destroys the shape of almost any distribution.