Design experiments that actually prove something. Most A/B tests fail because they test vague ideas, run too short, or peek at results. A well-designed experiment has a clear hypothesis, adequate power, and a pre-committed analysis plan.
Hypothesis Template
Every experiment starts with a written hypothesis before any work begins:
"If we [make this specific change] for [this audience], then [this metric] will [change in this direction] by [this amount], because [this reason based on evidence]."
Example:
"If we replace the 5-step onboarding wizard with a single guided first-project flow for new signups, then 7-day activation rate will increase from 23% to 35%, because 4/6 interviewed users said they wanted to 'just start using it' not 'set everything up first.'"
Every part matters:
- Specific change: Not "improve onboarding" — the exact change
- Audience: Who sees this? New users only? Free tier only?
- Metric + direction + amount: A number you'll measure
- Because: The evidence-based reason. No evidence = no experiment.
Experiment Design
1. Primary Metric
One metric the experiment is designed to move. Not three. One. Additional metrics are guardrails.
2. Guardrail Metrics
Metrics that must NOT degrade. These prevent "winning" by breaking something else.
3. Sample Size
Calculate BEFORE running. Use a sample size calculator with:
- Baseline conversion rate (current number)
- Minimum detectable effect (smallest change worth caring about)
- Statistical significance (95% is standard)
- Power (80% minimum)
If you need 50,000 users and you get 500/week, the experiment will take 100 weeks. Either increase the MDE or don't run the experiment.
4. Duration
Run for at least one full business cycle (usually 1-2 weeks minimum) to capture day-of-week effects. NEVER run less than 7 days.
5. Analysis Plan
Write BEFORE launching: what metric, what threshold, what you'll do if it wins/loses/is inconclusive. Pre-commit to avoid post-hoc storytelling.
Common Mistakes
- Peeking: Checking results daily and stopping when you see significance. This inflates false positive rates. Set a duration and don't peek until it's done.
- Underpowered tests: Running a test with too few users to detect a meaningful effect. The result is "inconclusive" which teaches you nothing.
- Testing too many things: Changing 5 things at once means you don't know what worked. One change per experiment.
- No guardrails: Increasing signups by removing the password field "works" but breaks everything else.
- Post-hoc storytelling: The test was inconclusive but "we noticed an interesting trend in the 25-34 age group." That's noise, not signal.
Guidelines
- CRITICAL: Write the hypothesis and analysis plan BEFORE building anything. If you can't state what you're testing and how you'll decide, you're not ready.
- NEVER peek at results before the pre-determined end date. Statistical significance at day 3 of a 14-day test is meaningless.
- NEVER run an experiment without calculating required sample size first.
- ALWAYS include guardrail metrics. Winning the primary metric while tanking retention is not a win.
- NEVER test more than one major change per experiment. Isolate variables.
- ALWAYS document learnings from inconclusive tests. "We couldn't detect an effect with N=5,000 users" is still useful information — it suggests the effect is small or nonexistent.
Built on controlled experimentation methodology (Kohavi, Tang, Xu). Skills from productskills.
