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.