SYS/COCKPIT
CRO tools
Pre-test, post-test reading and sequential peeking — the experimentation lab in one workspace.
MDE for a given n
Reverse power: with the traffic you have — as visitors, or as a rate × a duration (days, weeks, months) — what is the smallest detectable effect? Several MDEs are compared, and each one says whether it fits the window.
SYS/IN
SYS/OUT
Detectable MDE
1 pp
- in percentage points: 1 pp
- relative to the baseline rate: 20%
Available n per arm: 8,155 — Window covered by this n: 5.9 weeks
| MDE | n / arm | Duration (weeks) | With this traffic |
|---|---|---|---|
| 0.5 pp | 31,231 | 22.4 | Out of window |
| 1 pp | 8,155 | 5.9 | Covered |
| 2 pp | 2,210 | 1.7 | Covered |
| 10 % relative | 31,231 | 22.4 | Out of window |
| 20 % relative | 8,155 | 5.9 | Covered |
Bars: duration of each ticked scenario, in the unit you picked. The table is readable with JavaScript off.
How this is calculated
Method
Reverse power: given a budget of (n) visitors per arm and a baseline rate (p_1), what is the smallest effect still detectable at the chosen (\alpha) and power?
The calculation inverts the two-proportion z-test sample-size formula (bisection on the absolute MDE, monotone in (\delta)). The result is returned in the requested mode:
- Absolute: (\delta) in percentage points.
- Relative: (\delta / p_1), a fraction of the baseline.
Both readings are always shown together, so a “20%” cannot be mistaken for points. This is the honest question before launching a test: can this traffic even see the effect we hope for?
Sources
- Inverse of Evan Miller, Sample Size Calculator.
- Kohavi, Tang, Xu, Trustworthy Online Controlled Experiments (Cambridge, 2020), ch. 17.
Window and scenarios
n per arm can be typed in, or derived from a rate (visitors per day, week or month) times a duration in the unit you pick, then split equally across 2, 3 or 4 arms. A week is 7 days. A month is 365.25 / 12 days — an average, not a named month.
Each ticked scenario is a candidate MDE, absolute (points) or relative (a fraction of the baseline). For each one the tool computes the required n and the duration in the display unit, then says whether the available traffic covers it. The detectable MDE stays the power inversion on that traffic, not an average of the scenarios.
A one-sided test replaces (z_{\alpha/2}) with (z_{\alpha}). It is only justified when a loss does not have to be detected.
Limits
- Same model as binary sample size: independent visitors, fixed horizon, two arms, one metric.
- An (n) observed after the fact is not a pre-registered (n). If the test has already been peeked, this MDE is no longer the protocol’s MDE.
- This is not a “B wins” decision. It is a detectability floor under H0, not a verdict on an arm.