Minimum Detectable Effect
The smallest improvement in a metric that an experiment is designed to reliably detect with a given level of statistical power and significance, determining the practical sensitivity of the test.
The minimum detectable effect (MDE) defines the threshold of change your experiment can reliably identify. If your MDE is 5%, the experiment will detect a true improvement of 5% or larger but will likely miss smaller real effects. MDE is inversely related to sample size: detecting smaller effects requires more users and longer experiment duration.
For growth teams, MDE is the critical bridge between statistical requirements and business decisions. AI can help determine appropriate MDEs by modeling the business impact of different effect sizes and recommending the MDE that balances experiment duration against the value of detecting smaller improvements. Growth engineers should set MDE based on the minimum improvement that would justify the engineering cost of implementing the change permanently. If a 1% conversion improvement would generate significant revenue, the experiment needs enough users to detect a 1% change, which might require weeks of testing. If only improvements above 5% would be worth implementing, a shorter test with larger MDE is appropriate. Teams should document the MDE for every experiment and acknowledge that non-significant results mean the true effect is smaller than the MDE rather than that there is no effect at all.
Related Terms
Event Tracking
The practice of recording specific user interactions within a digital product, such as clicks, form submissions, page views, and feature usage, as structured data events that can be analyzed to understand user behavior.
Event Taxonomy
A structured naming convention and classification system for analytics events that ensures consistency, discoverability, and usability of tracking data across teams, platforms, and analysis tools.
Funnel Analysis
The process of tracking and measuring user progression through a defined sequence of steps toward a conversion goal, identifying where users drop off and quantifying the conversion rate between each stage.
Conversion Rate Analytics
The systematic measurement and analysis of the percentage of users who complete a desired action out of the total who had the opportunity, applied across multiple conversion points throughout the user journey.
Drop-Off Rate
The percentage of users who leave a process or sequence at a specific step without completing the next step, the inverse of step-level conversion rate, used to identify friction points in user flows.
Cohort Analysis
A technique that groups users by a shared characteristic or experience within a defined time period and tracks their behavior over subsequent periods, revealing how user behavior evolves and differs across groups.