3 min read
Statistical significance explained without any maths
What the number actually claims, what it doesn't, and the four sentences you need to read a result honestly without ever computing anything.
Topic
Articles on Sample size
Everything we have published on Sample size: guides, experiments and what we learned along the way.
13 articles
3 min read
More than two variants: when an A/B/C actually makes sense
Adding a third variant costs you a third of your sample and buys you a comparison you may not need. Three cases where it's worth it, and one very common case where it isn't.
3 min read
Traffic allocation: when to use 50/50 and when to use 90/10
An even split gets you the fastest answer. An uneven one limits how many customers see a change you're not sure about. You can't have both.
3 min read
Mutually exclusive tests: stopping two experiments from contaminating each other
Two experiments on the same funnel produce four combinations and one confused conclusion. Mutual exclusion is how you keep the answer readable.
3 min read
Supertargeting: combining country, device, source and behaviour
Stacking conditions makes the segment sharper and the sample smaller. Here's how to know when the trade is worth it — and when you've segmented yourself into noise.
3 min read
New vs returning visitors: two websites inside the same website
A first-time visitor needs to be convinced. A returning one needs to be let through. Showing both the same page is the most common unforced error in CRO.
3 min read
Geographic targeting: why the same test doesn't work in two countries
Shipping expectations, payment habits, price sensitivity and trust signals vary by market. Averaging across them is how you get neutral results that hide real effects.
4 min read
How much traffic do you need for A/B testing?
The honest answer has three inputs and no marketing spin. Here's how to calculate it for your own site, and what to do when the number comes back too big.
4 min read
What to test first when your site doesn't have much traffic
Low traffic doesn't mean you can't experiment. It means you can only detect large effects — so you have to test the things capable of producing them.
2 min read
What to Do When a Test Returns Inconclusive Results
Your A/B test came back flat? A tie isn't a failure. Learn how to read an inconclusive result, what to do next, and how to turn it into real learning.
2 min read
Mistakes that invalidate your A/B tests (and how to avoid them)
A poorly run A/B test is worse than no test at all: it gives you false confidence. Here are the most common mistakes that invalidate results and how to prevent them.
2 min read
Sample size: how to know if your test is reliable
Sample size quietly ruins more experiments than any other factor. Learn the three variables behind it and how to know if your A/B test can actually reach a conclusion.
2 min read
How Long Should You Run an A/B Test
Stop too early and you crown false winners; run too long and you waste traffic. Learn how to set the right A/B test duration before you launch.
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