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Confidence interval

A confidence interval is the range your true result probably sits in, given that you only measured a sample. If an A/B test reports a 12% lift with a 95% confidence interval of 4% to 20%, it's saying the real lift is likely somewhere in that band, not exactly 12%. The wider the band, the less you actually know.

For a founder this is the antidote to over-reading a single number. A flattering point estimate with a band that crosses zero means the effect might be nothing at all. Smaller samples give wider intervals, which is why early results swing wildly and shouldn't drive big bets. The practical habit: never quote a conversion lift or metric change without the interval, and treat any result whose band includes 'no change' as unproven. It pairs with statistical significance, the interval shows the size and uncertainty, significance shows whether it's real.

Articles

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    Statistical significance is just the beginning. Learn how to interpret results correctly, avoid false positives, and turn winning experiments into permanent improvements across your growth engines.

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    Most experiments fail before they start because the hypothesis is vague or untestable. Learn how to write hypotheses that are specific enough to prove or disprove and tied to metrics that matter.

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    A folder full of interview notes is worthless if nothing changes. Learn how to spot patterns across conversations and turn what you heard into better copy, sharper ads, and stronger sales conversations.

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    A winning test means nothing if the setup was flawed. Learn how to configure experiments properly in VWO, ad platforms, and email tools so your results are actually valid.

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    Random testing wastes time and teaches you nothing. Learn how to collect experiment ideas systematically and prioritise them based on potential impact so you always know what to run next.

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    Build a knowledge base from past experiments so new tests build on proven insights instead of starting from scratch every time.

All 20 articles under A/B testing and experimentation
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