Minimum Detectable Effect
The smallest improvement in conversion rate that an A/B test is designed to detect with statistical reliability, given a fixed sample size, confidence level, and statistical power. Calculating MDE before launching a test tells you whether your traffic volume is sufficient to measure a meaningful change. Testing for a 2% lift on a low-traffic page requires months of data. On those pages, focus on high-confidence qualitative changes rather than formal A/B testing.
How Minimum Detectable Effect works in practice
Minimum Detectable Effect matters most when teams are trying to make better decisions around landing page clarity, conversion friction, trust, and user decision-making. The short definition gives the surface meaning, but the practical value comes from knowing when this concept should actually influence strategy and when it should not.
In real-world work, Minimum Detectable Effect is rarely important on its own. It usually becomes useful when paired with cleaner measurement, stronger page or funnel structure, and a clear understanding of what business outcome needs to improve. It is closely connected to A/B Testing, Statistical Significance, Confidence Interval because those concepts usually shape how Minimum Detectable Effect is measured or applied in practice.
A good way to use Minimum Detectable Effect is to treat it as a decision aid rather than a vanity number. If it helps explain why performance is improving, stalling, or getting more expensive, it is useful. If it is being tracked without any operational consequence, it is probably being overvalued.
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Let's talk →This term sits in the CRO category, which means it is most useful when evaluating landing page clarity, conversion friction, trust, and user decision-making. The goal is not to memorize the label. The goal is to know when it should change a decision, a page, a campaign, or a measurement setup.
Related terms
A controlled experiment comparing two versions of a page, ad, or email to determine which performs better for a defined metric. Statistical significance is required before declaring a winner and rolling out changes.
A measure of confidence that an observed difference between test variants is not due to random chance. In A/B testing, results are typically considered significant at the 95% confidence level, meaning there is only a 5% probability the observed lift happened by chance. Stopping a test before reaching significance is one of the most common and costly mistakes in CRO — the result looks like a win but the lift evaporates once the test ends.
A range of values within which the true effect of a test variant likely falls, at a given confidence level. A 95% confidence interval of +2% to +8% lift means you can be 95% confident the true conversion rate improvement lies somewhere in that range. Narrower intervals require larger sample sizes. Reporting a point estimate ("we got 5% lift") without the confidence interval hides the uncertainty in the result.
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