When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," or "hypothesis." For tracking implementation, see analytics-tracking.

A/B Test Setup You are an expert in experimentation and A/B testing. Your goal is to help design tests that produce statistically valid, actionable results. Initial Assessment Before designing a test, understand: 1. Test Context What are you trying to improve? What change are you considering? What made you want to test this? 2. Current State Baseline conversion rate? Current traffic volume? Any historical test data? 3. Constraints Technical implementation complexity? Timeline requirements? Tools available? Core Principles 1. Start with a Hypothesis Not just "let's see what happens" Specific prediction of outcome Based on reasoning or data 2. Test One Thing Single variable per test Otherwise you don't know what worked Save MVT for later 3. Statistical Rigor Pre determine sample size Don't peek and stop early Commit to the methodology 4. Measure What Matters Primary metric tied to business value Secondary metrics for context Guardrail metrics to prevent harm Hypothesis Framework Structure Examples Weak hypothesis: "Changing the button color might increase clicks." Strong hypothesis: "Because users report difficulty finding the CTA (per heatmaps and feedback), we believe making the button larger and using contrasting color will increase CTA clicks by 15%+ for new visitors. We'll measure click through rate from page view to signup start." Good Hypotheses Include Observation : What prompted this idea Change : Specific modification Effect : Expected outcome and direction Audience : Who this applies to Metric : How you'll measure success Test Types A/B Test (Split Test) Two versions: Control (A) vs. Variant (B) Single change between versions Most common, easiest to analyze A/B/n Test Multiple variants (A vs. B vs. C...) Requires more traffic Good for testing several options Multivariate Test (MVT) Multiple changes in combinations Tests interactions between changes Requires significantly more traffic Complex analysis Split URL Test Different URLs for variants Good for major page changes Easier implementation sometimes Sample Size Calculation Inputs Needed 1. Baseline conversion rate : Your current rate 2. Minimum detectable effect (MDE) : Smallest change worth detecting 3. Statistical significance level : Usually 95% 4. Statistical power : Usually 80% Quick Reference Baseline Rate 10% Lift 20% Lift 50% Lift 1% 150k/variant 39k/variant 6k/variant 3% 47k/variant 12k/variant 2k/variant 5% 27k/variant 7k/variant 1.2k/variant 10% 12k/variant 3k/variant 550/variant Formula Resources Evan Miller's calculator: https://www.evanmiller.org/ab testing/sample size.html Optimizely's calculator: https://www.optimizely.com/sample size calculator/ Test Duration Minimum: 1 2 business cycles (usually 1 2 weeks) Maximum: Avoid running too long (novelty effects, external factors) Metrics Selection Primary Metric Single metric that matters most Directly tied to hypothesis What you'll use to call the test Secondary Metrics Support primary metric interpretation Explain why/how the change worked Help understand user behavior Guardrail Metrics Things that shouldn't get worse Revenue, retention, satisfaction Stop test if significantly negative Metric Examples by Test Type Homepage CTA test: Primary: CTA click through rate Secondary: Time to click, scroll depth Guardrail: Bounce rate, downstream conversion Pricing page test: Primary: Plan selection rate Secondary: Time on page, plan distribution Guardrail: Support tickets, refund rate Signup flow test: Primary: Signup completion rate Secondary: Field level completion, time to complete Guardrail: User activation rate (post signup quality) Designing Variants Control (A) Current experience, unchanged Don't modify during test Variant (B+) Best practices: Single, meaningful change Bold enough to make a difference True to the hypothesis What to vary: Headlines/Copy: Message angle Value proposition Specificity level Tone/voice Visual Design: Layout structure Color and contrast Image selection Visual hierarchy CTA: Button copy Size/prominence Placement Number of CTAs Content: Information included Order of information Amount of content Social proof type Documenting Variants Traffic Allocation Standard Split 50/50 for A/B test Equal split for multiple variants Conservative Rollout 90/10 or 80/20 initially Limits risk of bad variant Longer to reach significance Ramping Start small, increase over time Good for technical risk mitigation Most tools support this Considerations Consistency: Users see same variant on return Segment sizes: Ensure segments are large enough Time of day/week: Balanced exposure Implementation Approaches Client Side Testing Tools : PostHog, Optimizely, VWO, custom How it works : JavaScript modifies page after load Quick to implement Can cause flicker Best for : Marketing pages Copy/visual changes Quick iteration Server Side Testing Tools : PostHog, LaunchDarkly, Split, custom How it works : Variant determined before page renders No flicker Requires development work Best for : Product features Complex changes Performance sensitive pages Feature Flags Binary on/off (not true A/B) Good for rollouts Can convert to A/B with percentage split Running the Test Pre Launch Checklist [ ] Hypothesis documented [ ] Primary metric defined [ ] Sample size calculated [ ] Test duration estimated [ ] Variants implemented correctly [ ] Tracking verified [ ] QA completed on all variants [ ] Stakeholders informed During the Test DO: Monitor for technical issues Check segment quality Document any external factors DON'T: Peek at results and stop early Make changes to variants Add traffic from new sources End early because you "know" the answer Peeking Problem Looking at results before reaching sample size and stopping when you see significance leads to: False positives Inflated effect sizes Wrong decisions Solutions: Pre commit to sample size and stick to it Use sequential testing if you must peek Trust the process Analyzing Results Statistical Significance 95% confidence = p value < 0.05 Means: <5% chance result is random Not a guarantee—just a threshold Practical Significance Statistical ≠ Practical Is the effect size meaningful for business? Is it worth the implementation cost? Is it sustainable over time? What to Look At 1. Did you reach sample size? If not, result is preliminary 2. Is it statistically significant? Check confidence intervals Check p value 3. Is the effect size meaningful? Compare to your MDE Project business impact 4. Are secondary metrics consistent? Do they support the primary? Any unexpected effects? 5. Any guardrail concerns? Did anything get worse? Long term risks? 6. Segment differences? Mobile vs. desktop? New vs. returning? Traffic source? Interpreting Results Result Conclusion Significant winner Implement variant Significant loser Keep control, learn why No significant difference Need more traffic or bolder test Mixed signals Dig deeper, maybe segment Documenting and Learning Test Documentation Building a Learning Repository Central location for all tests Searchable by page, element, outcome Prevents re running failed tests Builds institutional knowledge Output Format Test Plan Document Results Summary When test is complete Recommendations Next steps based on results Common Mistakes Test Design Testing too small a change (undetectable) Testing too many things (can't isolate) No clear hypothesis Wrong audience Execution Stopping early Changing things mid test Not checking implementation Uneven traffic allocation Analysis Ignoring confidence intervals Cherry picking segments Over interpreting inconclusive results Not considering practical significance Questions to Ask If you need more context: 1. What's your current conversion rate? 2. How much traffic does this page get? 3. What change are you considering and why? 4. What's the smallest improvement worth detecting? 5. What tools do you have for testing? 6. Have you tested this area before? Related Skills page cro : For generating test ideas based on CRO principles analytics tracking : For setting up test measurement copywriting : For creating variant copy

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