Definition
MDE is the smallest effect size you want your experiment to reliably detect. Smaller MDE requires much larger samples.
Example
If baseline conversion is 2%, an MDE of 0.3 points means detecting a lift to 2.3%.
How to use it
- Choose an MDE that is both realistic and action-worthy.
- Use absolute percentage points for conversion rates to avoid confusion.
- Align MDE with the cost of acting on a change.
- Revisit MDE as baseline rates change over time.
- Define MDE before the test so you can size the sample correctly.
- Use separate MDE targets for critical funnel steps if volume differs.
Common mistakes
- Setting MDE so low that the sample size is unattainable.
- Using relative percent when stakeholders expect absolute points.
- Choosing MDE after seeing early results.
- Using a generic MDE across very different funnels or segments.
Measured as
Measure Minimum Detectable Effect (MDE) with a fixed attribution window, conversion event, and spend basis before comparing campaigns or creative tests.
Misused when
- Setting MDE so low that the sample size is unattainable.
- Using relative percent when stakeholders expect absolute points.
- Choosing MDE after seeing early results.
- Using a generic MDE across very different funnels or segments.
Operator takeaway
- Choose an MDE that is both realistic and action-worthy.
- Use absolute percentage points for conversion rates to avoid confusion.
- Align MDE with the cost of acting on a change.
- Use Minimum Detectable Effect (MDE) only inside a stable attribution rule, conversion definition, and time window so campaign comparisons stay honest.
- If performance changes, check whether the metric moved for a real business reason or because the measurement setup changed underneath you.
Next decision
- Quantify the impact with A/B Test Sample Size Calculator if you need to turn the definition into an operating assumption.
- Read A/B test sample size: how to plan conversion experiments if the decision depends on interpretation, policy, or trade-offs beyond the raw formula.
Where to use this on MetricKit
Calculators
- A/B Test Sample Size Calculator: Estimate sample size per variant for a conversion rate A/B test given baseline CVR, MDE, significance, and power.
Guides
- A/B test sample size: how to plan conversion experiments: A practical guide to A/B test planning: baseline CVR, MDE, alpha, power, sample size, and common pitfalls like peeking.