---
name: Autonomous A/B Testing & Personalization
description: Analyzes real-time behavioral data, dynamically switches UI layout and CTA variants, tracks statistical significance against a pre-registered hypothesis, and rolls the winning variant out — with guards against p-hacking and peeking, consent-respecting personalization, and a human gate on irreversible or high-risk changes. Results are measured, not assumed. For growth, product, and experimentation teams.
audience: growth · product · experimentation
---

# Autonomous A/B Testing & Personalization

## What this is

A method for running experiments that hold up: a pre-registered hypothesis and success metric, variants of layout or CTA served against real behavioral data, significance tracked to a threshold fixed before the test starts, and — once a variant clears the bar honestly — a rollout to the full audience. Personalization segments respect consent and avoid discriminatory splits. The point is disciplined automation, not automation that finds a false winner faster.

## What this is NOT

Not a licence to declare winners early or optimize a metric into a lie. It requires a pre-registered hypothesis, a minimum sample, and a significance threshold set in advance — this is what guards against p-hacking and peeking, and those guards are not optional. Auto-deploy is gated: irreversible or high-risk changes (pricing, legal copy, anything a user can't undo) require a human sign-off before rollout. Results are measured against the pre-registered metric, never assumed, and no lift is guaranteed.

## Method

1. **Pre-register the hypothesis.** State the change, the primary metric, the minimum detectable effect, and the significance threshold before any traffic is split — a hypothesis written after the data is decoration.
2. **Size the sample.** Compute the minimum sample and duration for the chosen effect and threshold; a test that stops when it looks good is a peeking failure.
3. **Define the variants and guardrail metrics.** Specify each layout/CTA variant and the guardrail metrics (revenue, churn, complaints) that must not degrade while the primary metric moves.
4. **Check consent before personalizing.** Segment only on data the user consented to, and exclude splits that would discriminate on protected or sensitive attributes.
5. **Serve and observe.** Route real traffic to variants and collect behavioral data without inspecting significance mid-flight except through pre-declared interim rules.
6. **Test significance honestly.** Evaluate at the planned sample against the fixed threshold; correct for multiple comparisons; do not stop early on a favourable blip.
7. **Gate the rollout.** If the winner is a low-risk UI change, auto-deploy; if it touches pricing, legal copy, or anything irreversible, hold for human sign-off.
8. **Roll out and re-measure.** Ship the winner to the full audience, watch the guardrail metrics post-rollout, and be ready to revert if the effect doesn't hold.

## Quality bar

Hypothesis, metric, minimum sample, and threshold are pre-registered · no early stopping or peeking outside declared rules · multiple comparisons corrected · guardrail metrics watched · personalization is consent-respecting and non-discriminatory · high-risk / irreversible changes gated to human sign-off · results measured against the pre-registered metric, never assumed · post-rollout re-measured with a revert path.

## Guardrails & escalation

The statistical guards — pre-registration, minimum sample, fixed threshold, no peeking, multiple-comparison correction — are mandatory; skipping them manufactures false winners. Auto-deploy is limited to low-risk, reversible UI changes; pricing, legal copy, and any irreversible or high-risk change require explicit human sign-off before rollout. Personalization respects consent and avoids discriminatory segmentation; questions about lawful use of behavioral data or protected attributes route to privacy and legal owners. No lift is guaranteed.

## References

- Catalogue: https://edwson.com/consumer-design-system.html · Contracts: https://edwson.com/cds/components.json · Agent brief: https://edwson.com/cds/AGENTS.md
- Related within this kit: the user-journey & friction map (what to test), the dark-pattern & trust audit (personalization must stay honest), and Google Analytics. Data-use questions route to privacy and counsel.