Experiment Plan: [feature / change]

A/B test design – decide the success criteria before looking at the data, or you will rationalize whatever you see.

Field	Value
Experiment	[name]
Owner	@name
Analyst	@name
Start	YYYY-MM-DD
Planned end	YYYY-MM-DD
Status	Design / Running / Decided

Hypothesis

We believe [change] will cause [metric] to [direction] by [size] because [mechanism].

Variants

Variant	Change	Allocation
Control	current experience	N%
A	[change]	N%

Metrics

Type	Metric	Direction	Threshold
Primary	[e.g. activation rate]	up	>= +N%
Guardrail	[e.g. churn]	flat or better	<= +N%
Diagnostic	[e.g. click-through]	informational	–

Sizing

• Baseline conversion: N%
• Minimum detectable effect: N%
• Significance: 95%; power: 80%
• Required sample per arm: $n = \frac{16\sigma^2}{\delta^2}$ = N
• Daily traffic: N -> expected duration: N days

Commit to the duration now. Peeking early and stopping on a spike is the most common way experiments lie.

Segmentation & exclusions

• Included: [population]
• Excluded: [internal users, bots, ...]
• Pre-declared subgroups to analyze: [list]

Decision rule

Outcome	Action
Primary up, guardrails flat	Ship variant
Primary flat	Revert; record learning
Guardrail broken	Stop immediately, revert

Results (fill after)

Variant	Primary metric	Guardrail	Verdict
Control
A

Learnings

What this changes in the roadmap: