{"uid":"cap_Vs9asKyPZfOBp4CKai8SQ","slug":"marketing-experiment-a-b-test-significance-calculator-f8cd133f","name":"Marketing Experiment A/B Test Significance Calculator","description":"A/B test significance with a two-proportion z-test: observed lift, z-score, two-sided p-value, confidence interval on the lift, minimum detectable effect, and sample-size sufficiency. Pure computation over your inputs.","url":"https://payai.agentstools.dev/marketing/experiment","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"a":{"type":"object","description":"Control arm with conversions and visitors"},"b":{"type":"object","description":"Variant arm with conversions and visitors"},"alpha":{"type":"number","description":"Significance level, default 0.05"},"power":{"type":"number","description":"Target power for sample-size math, default 0.8"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.02","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.02/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.02","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.02","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_ULLG7J3Chi6SjLVPklmYf","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.02","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Runs a two-proportion z-test on A/B experiment data to return observed lift, z-score, p-value, confidence interval, minimum detectable effect, and sample-size sufficiency.","exampleAgentPrompt":"Can you check if my A/B test is statistically significant? The control arm had 4,200 visitors with 310 conversions, and the variant had 4,150 visitors with 365 conversions — use alpha 0.05 and target power of 0.8.","exampleUseCases":[{"title":"Landing page conversion rate test","prompt":"I ran a landing page experiment — control had 8,000 visitors and 640 conversions, variant had 8,100 visitors and 720 conversions. Is the lift statistically significant at the 95% confidence level with power 0.8?"},{"title":"Email campaign click-through experiment","prompt":"My email A/B test is done: version A (control) got 12,500 sends and 875 clicks, version B (variant) got 12,300 sends and 1,010 clicks. Can you compute the z-score, p-value, and confidence interval on the lift, and tell me if the sample size was sufficient?"},{"title":"Checkout flow variant decision","prompt":"We tested a new checkout flow — the old one had 5,500 visitors with 412 purchases, the new one had 5,450 visitors with 489 purchases. Run the two-proportion z-test at alpha 0.01 and tell me if we have enough evidence to roll it out."}],"resultDescription":"Returns the observed lift between control and variant conversion rates, the z-score from the two-proportion z-test, a two-sided p-value, a confidence interval on the lift, the minimum detectable effect given the sample sizes, and a boolean or verdict on whether the sample size is sufficient for the specified power level.","failureModes":["Missing or malformed 'a' or 'b' arm objects (conversions or visitors fields absent)","Visitors count of zero causing division-by-zero","Conversions exceeding visitors count (invalid input)","Alpha or power values outside valid range (0,1)","Extremely small sample sizes producing unreliable z-test approximations"],"whenToPreferThis":"Use this endpoint when you need fast, pure-computation statistical significance testing for a two-arm conversion-rate experiment without spinning up a data science environment. It is ideal for agents that need to make ship/no-ship decisions based on A/B test results, or for reporting pipelines that must interpret experiment outcomes automatically. Prefer it over general-purpose statistics libraries when you want a single API call that bundles lift, z-score, p-value, CI, MDE, and sample-size sufficiency in one response.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T07:01:07.921Z","isFirstParty":false}