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Experiment Results Guardrails

Say whether a test can be trusted yet, before anyone declares a winner.

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What it adds

A results view that reports sample sufficiency, exposure quality, and effect size alongside the numbers — and refuses to crown a winner early.

What your agent is told to do

5
  1. 1

    Compute results from exposure events — users who actually saw the variant — not from assignment counts. Assigned-but-never-exposed users dilute every rate.

  2. 2

    Fix the primary metric and the stopping rule when the experiment is created, and version them. A metric changed after the data arrives is not a result.

  3. 3

    Show a confidence interval and the absolute effect next to every relative lift. A 40% improvement on a 0.1% baseline is noise dressed as a win.

  4. 4

    Compute the sample size needed for the stated minimum detectable effect, and show progress towards it prominently.

  5. 5

    Do NOT display a winner, a green badge, or a significance verdict before the stopping rule is met. Show the guardrail instead and say why.

Edge cases it handles

6
  • Sample ratio mismatch — buckets arriving at meaningfully different sizes — means assignment is broken. Detect it and invalidate the readout rather than interpreting the numbers.
  • Repeated checking inflates false positives. Either record how many times results were viewed and warn, or use a method that tolerates peeking.
  • The current day and the first hours of an experiment are partial. Exclude or mark them rather than letting them swing the totals.
  • Weekday and weekend traffic behave differently. Warn when an experiment has not covered whole weekly cycles.
  • Testing many secondary metrics will find something significant by chance. Label secondary metrics as exploratory and adjust or say you have not.
  • A guardrail metric moving the wrong way — errors, latency, refunds — must be surfaced even when the primary metric wins.

Definition of done

9
  • Rates are computed from exposure events, not assignment records.
  • The primary metric and stopping rule are fixed at creation and versioned on change.
  • Required sample size and progress towards it are shown on the results view.
  • Absolute effect and a confidence interval accompany every relative lift.
  • Sample ratio mismatch is detected and blocks the readout.
  • No winner is declared before the stopping rule is satisfied.
  • Guardrail metrics are shown regardless of the primary result.
  • The feature matches the existing design system.
  • No existing functionality is broken.

Related features

How it works

  1. 1

    Copy the link

    Grab the Markdown instruction URL for this feature.

  2. 2

    Give it to your AI

    Paste it into Claude Code, Cursor, v0, Lovable — whatever you build with.

  3. 3

    It inspects, then implements

    Your agent reads your existing app first, then adds the feature to fit it.

Works with your stack

These instructions are written to adapt. They tell the agent to detect your framework, match your existing design system, and reuse what you already have — rather than assuming a particular stack.

Need it tighter than that? Customize the feature and tell it exactly what you're running.