Common Experimentation Pitfalls
Avoid these mistakes that invalidate results or lead to wrong conclusions.
Statistical Mistakes
1. Stopping Early (Peeking)
The problem: Checking results daily and stopping when you see significance.
Why it's wrong: Statistical significance fluctuates. At any point during a test, you might see "significance" that disappears with more data. This is called the "peeking problem" or "repeated significance testing."
The fix:
- Pre-calculate required sample size
- Commit to running until you reach it
- If you must peek, use sequential testing methods that account for multiple looks
2. Underpowered Tests
The problem: Running tests without enough traffic to detect realistic effect sizes.
Why it's wrong: You'll conclude "no difference" when there actually is one—you just couldn't detect it.
The fix:
- Calculate required sample size before starting
- Be realistic about minimum detectable effect (can you act on a 0.5% improvement?)
- If traffic is low, test bigger changes
3. Multiple Comparisons
The problem: Testing many variants or metrics and celebrating any that reach significance.
Why it's wrong: With 20 metrics, you expect 1 false positive at 95% confidence—by chance alone.
The fix:
- Define ONE primary metric before starting
- Use Bonferroni correction or similar for multiple comparisons
- Treat secondary metrics as directional, not conclusive
4. Ignoring Segments
The problem: Only looking at aggregate results.
Why it's wrong: Simpson's Paradox—overall winner might be loser for your key segments.
The fix:
- Always segment by device, traffic source, user type
- Check if results are consistent across segments
- If segments differ dramatically, investigate why
Design Mistakes
5. Testing Too Many Things
The problem: Changing headline, image, CTA, and layout simultaneously.
Why it's wrong: You won't know which change caused the result. And each variable multiplies required sample size.
The fix:
- Test one variable at a time (A/B testing)
- If testing multiple, use proper multivariate testing with adequate sample size
- Prioritize highest-impact changes first
6. Vague Hypothesis
The problem: "Let's see if this new design is better."
Why it's wrong: Without a hypothesis, you can't learn WHY something worked (or didn't).
The fix:
- State: "We believe [change] will [impact metric] because [reasoning]"
- Even if you're wrong, you learn something
7. No Control
The problem: Changing the control during the test, or not having one.
Why it's wrong: You need a stable baseline to compare against.
The fix:
- Never modify the control mid-test
- If you must change it, start a new test
- Document exactly what the control is
Execution Mistakes
8. External Contamination
The problem: Running a test during a sale, holiday, or major event.
Why it's wrong: External factors affect both variants differently, contaminating results.
The fix:
- Avoid tests during unusual periods
- If unavoidable, note it and extend the test past the event
- Compare to the same period historically
9. Selection Bias
The problem: Testing on a non-representative sample (e.g., only logged-in users).
Why it's wrong: Results won't generalize to your full audience.
The fix:
- Test on representative traffic
- Be explicit about who's included/excluded
- Note limitations when reporting results
10. Implementation Bugs
The problem: Variants don't render correctly, tracking fires incorrectly, assignment is biased.
Why it's wrong: You're not testing what you think you're testing.
The fix:
- QA both variants thoroughly before launch
- Verify tracking events fire correctly
- Check assignment distribution matches weights
Interpretation Mistakes
11. Celebrating Trivial Wins
The problem: Implementing a change because it was "statistically significant" even though the effect was tiny.
Why it's wrong: Statistical significance ≠ practical significance. A 0.01% improvement isn't worth the complexity.
The fix:
- Define minimum meaningful effect before starting
- Consider implementation cost vs. benefit
- Don't over-optimize
12. Ignoring Confidence Intervals
The problem: Only reporting point estimates ("5% improvement!").
Why it's wrong: The true effect could be anywhere in the confidence interval.
The fix:
- Report confidence intervals: "5% improvement (95% CI: 2%-8%)"
- Base decisions on the lower bound for conservative estimates
- Wider intervals = more uncertainty
13. Not Documenting Learnings
The problem: Running tests but not recording what you learned.
Why it's wrong: You'll repeat mistakes, forget context, lose institutional knowledge.
The fix:
- Document every test: hypothesis, results, learnings
- Include what surprised you
- Build a searchable knowledge base
Organizational Mistakes
14. HiPPO (Highest Paid Person's Opinion)
The problem: Running experiments but ignoring results when leadership disagrees.
Why it's wrong: Defeats the purpose of data-driven decision making.
The fix:
- Get buy-in before testing that results will be honored
- Present data clearly to stakeholders
- Frame as "learning" not "winning/losing"
15. Testing Everything
The problem: Running experiments on trivial changes that don't matter.
Why it's wrong: Wastes resources, creates testing fatigue, delays important experiments.
The fix:
- Prioritize tests by potential impact
- Not everything needs a test—use judgment for low-risk changes
- Focus experimentation resources on high-value decisions
16. Sample Ratio Mismatch (SRM)
The problem: The actual traffic split doesn't match the intended split (e.g., you expect 50/50 but observe 52/48).
Why it's wrong: SRM is a strong signal of an implementation bug — broken randomization, bot contamination, or redirect issues. Results from experiments with SRM cannot be trusted.
The fix:
- Check the actual split ratio against expected before analyzing results
- Use a chi-squared test to detect statistically significant mismatches
- If SRM is detected, investigate the root cause before drawing any conclusions
- Common causes: bot traffic, browser redirects dropping users, bucketing bugs
17. Novelty and Primacy Effects
The problem: Users react differently to new designs initially, and the effect fades over time.
Why it's wrong: Short experiments may show inflated effects that don't persist. Returning users may click more simply because something looks new.
The fix:
- Run experiments for at least 2 full business cycles
- Segment results by new vs. returning users
- If possible, check whether the effect holds in the second week vs. the first