git:20260828.f80dcb5 to git:20260901.b973f20

---

name: experimentation

description: Designs, runs, and reads A/B tests and growth experiments — hypothesis, sample size, duration, and honest interpretation. Use this to plan a test, judge whether a result is real, build an experimentation program, decide what to test next, or diagnose why tests keep producing inconclusive or non-replicating results.

---

# Experimentation

Most A/B testing programs produce confident conclusions from insufficient data. The discipline is

almost entirely in what you do before launch.

## Before running

- **Hypothesis with a mechanism.** "Moving the pricing table above the fold will raise trial starts,

because visitors currently leave before seeing pricing." Not "let's try a green button."

- **One primary metric**, chosen in advance. Secondary metrics are context, never the verdict.

- **Sample size calculated in advance**, from your baseline rate and the smallest lift that would

change a decision. If the required sample is unreachable, do not run the test — decide by judgment

and say so.

- **Duration set in advance**, covering at least one full weekly cycle, and two if the buying cycle

is long.

- **Guardrail metrics** that would make you reject a win: refunds, support volume, downstream

retention.

## While running

Do not look at results and act on them mid-flight. Peeking and stopping at significance is the

single most common way to generate false positives, and it is very effective at it.

Check only that the test is running correctly — even split, no broken variant, tracking firing.

## Reading

- **At the pre-set duration**, not before, and not extended because it is nearly significant.

Extending until significance manufactures it.

- **Significance is not size.** A statistically significant 0.3% lift may not be worth shipping.

- **Inconclusive is a real result** and the most common one. It means the change did not matter

enough to detect, which is useful.

- **Check the guardrails** before declaring a win.

- **Segment afterward for hypotheses only**, never for verdicts. Slice enough ways and something is

always significant.

## Program level

Test where the traffic and the leverage are. Most sites can only run a handful of adequately powered

tests a year — spend them on structural questions, not button colors.

Keep a log of every test: hypothesis, result, decision. Without it, teams re-run the same tests every

eighteen months and re-learn the same things.