Question 1

How much traffic do I need for A/B testing?

Accepted Answer

Generally 1000+ visitors per variation for meaningful results. Use sample size calculators. Low traffic? Test bigger changes. Very low traffic? Use qualitative methods instead.

Question 2

What should I test first?

Accepted Answer

High-impact elements: headlines, CTAs, hero sections, pricing pages. Test where traffic and conversion opportunity intersect. Don't test button colors—test messaging.

Question 3

How long should I run tests?

Accepted Answer

Until statistical significance (usually 95% confidence). Minimum 1-2 weeks to account for day-of-week variation. Don't stop early just because one variant is ahead.

Question 4

What's statistical significance?

Accepted Answer

The probability that results aren't due to random chance. 95% confidence means 5% chance you're wrong. Don't celebrate "winners" before reaching significance.

Question 5

Which A/B testing tool should I use?

Accepted Answer

PostHog: developer-friendly, bundled with analytics. VWO/Optimizely: marketer-friendly, visual editor. Statsig: for feature flags too. Google Optimize: free but basic. Match to technical comfort.

Question 6

What's the difference between A/B and multivariate testing?

Accepted Answer

A/B tests one change. Multivariate tests multiple elements simultaneously. MVT needs more traffic. Start with A/B—simpler to interpret and requires less volume.

Question 7

How do I generate test hypotheses?

Accepted Answer

User research, heatmaps, session recordings, support tickets, competitor analysis. Frame as: "If we change X, Y will improve because Z." Hypothesis-driven testing beats random changes.

Question 8

What's a good uplift to expect?

Accepted Answer

5-15% improvements are typical wins. Bigger gains come from radical changes, not tweaks. Many tests fail—that's okay. Learning from losses is valuable.

Question 9

Should I test in development or production?

Accepted Answer

Production with real users. Development tests find bugs, not conversion insights. Use percentage rollouts to minimize risk. Your real audience's behavior is what matters.

Question 10

How do I avoid common testing mistakes?

Accepted Answer

Don't stop tests early. Don't test too many things at once. Ensure adequate sample size. Account for seasonality. Don't let HiPPOs (highest paid person's opinion) override data.

Question 11

What's feature flagging vs. A/B testing?

Accepted Answer

Feature flags control who sees what (rollouts, permissions). A/B testing measures impact. Often used together: flag controls the test, analytics measures results. Tools like Statsig do both.

Question 12

How do I prioritize which tests to run?

Accepted Answer

ICE framework: Impact × Confidence × Ease. High-impact, high-confidence, easy-to-run tests first. Build a backlog. Running any test is better than debating which to run.

Question 13

Can I A/B test emails?

Accepted Answer

Yes—subject lines, send times, CTAs, content length. Most email tools have built-in A/B testing. Emails are great for testing because results come quickly.

Question 14

How do I communicate test results?

Accepted Answer

Share wins AND losses. Include confidence level and sample size. Explain business impact (not just percentage). Build a culture of experimentation, not just winning.

Question 15

What if my test is inconclusive?

Accepted Answer

Learn something anyway: neither variant clearly better means both are acceptable. Consider if test was big enough change. Move on to bigger opportunities. Not all tests produce winners.

I need to test and optimize my website for conversions

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