[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"cms-page:\u002Fresources\u002Ftools\u002Fbayesian-ab-test-cro-calculator":3},{"date":4,"date_gmt":4,"content":5,"categories":7,"coauthors":8,"class_list":10,"acf":17,"head_links":188,"breadcrumbs":189,"head":199},"2026-09-19T20:16:08",{"rendered":6},"\u003Cp>[calculator-cro]\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"how-to-read-this-bayesian-a-b-calculator\">How to read this Bayesian A\u002FB calculator\u003C\u002Fh2>\n\n\n\n\u003Cp>Use this when you already have visitors and conversions for a control (A) and a variant (B), and you want a practical answer to: how likely is B’s conversion rate higher than A’s, and what do you stand to lose if you pick the wrong one?\u003C\u002Fp>\n\n\n\n\u003Cp>It is not a sample-size planner, and it is not a p-value calculator. \u003C\u002Fp>\n\n\n\n\u003Cp>What to enter\u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>Visitors and conversions for A and B. Conversions cannot exceed visitors.\u003C\u002Fli>\n\n\n\n\u003Cli>Credible interval (CI) — how wide the posterior conversion-rate range in the table is (90%, 95% or 99%). This does not change chance-to-beat.\u003C\u002Fli>\n\n\n\n\u003Cli>Winner threshold — the bar for naming a winner (90%, 95% or 99%). Until one variation’s chance-to-beat clears that bar, you will see “No winner yet”. That is a decision rule, not a law of nature.\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>Share results copies a link with those inputs (not a frozen “winner”), so anyone opening it gets the same recalculation.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"how-to-read-the-output\">How to read the output\u003C\u002Fh2>\n\n\n\n\u003Cp>Chance-to-beat is the probability that one variation’s true conversion rate is higher than the other’s, given these counts and a uniform prior. The two bars add up to (about) 100%. A 92% chance B is better is strong evidence, not a guarantee — and at a 95% winner threshold it is still “no winner yet”.\u003C\u002Fp>\n\n\n\n\u003Cp>CR in the table is the observed rate: conversions ÷ visitors. That is the number you would quote from analytics. It is not the Bayesian posterior mean, which is slightly pulled toward 50% on small samples.\u003C\u002Fp>\n\n\n\n\u003Cp>Uplift is the observed relative change of B versus A: (CR B − CR A) ÷ CR A. On 100 conversions from 1,000 visitors vs 120 from 1,000, that is +20%. Treat it as a description of what you saw, not a promise of what you will get if you roll out.\u003C\u002Fp>\n\n\n\n\u003Cp>CI is the posterior range for each variation’s conversion rate at the level you chose. Wide intervals mean the chance-to-beat can still move a lot with more data. We only warn when an interval is wider than five percentage points.\u003C\u002Fp>\n\n\n\n\u003Cp>Expected loss (in percentage points) is the average conversion-rate cost if you pick that arm and it is actually worse. It is the useful “what if we are wrong?” number. A high chance-to-beat with tiny expected loss on B is a much more comfortable rollout than the same chance-to-beat with a large loss if B is wrong.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"a-worked-example\">A worked example\u003C\u002Fh2>\n\n\n\n\u003Cp>Default numbers: A 1,000 visitors \u002F 100 conversions (10% CR); B 1,000 \u002F 120 (12% CR).\u003C\u002Fp>\n\n\n\n\u003Cp>You should see roughly a 92% chance B is higher, +20% observed uplift, and no winner at a 95% threshold. Expected loss if you choose B is small; expected loss if you stick with A is about two percentage points of conversion rate. That is a “B is probably better, but we have not crossed our own bar yet” read — which is more honest than calling it a win.\u003C\u002Fp>\n\n\n\n\u003Cp>Raise B to 150 conversions and the same threshold will typically name B as the winner, with observed CR 50% higher than A.\u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"when-to-be-careful\">When to be careful\u003C\u002Fh3>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>Unequal traffic. The SRM warning only compares visitor counts to an assumed 50\u002F50 split. Ignore it if you planned 80\u002F20 (or any other split). Take it seriously if you did plan equal traffic.\u003C\u002Fli>\n\n\n\n\u003Cli>No conversions yet. Chance-to-beat is mostly prior noise until both arms have completions.\u003C\u002Fli>\n\n\n\n\u003Cli>100% conversion rate on an arm, or very unequal sample sizes, make the posterior awkward. The calculator still runs; treat the charts as indicative.\u003C\u002Fli>\n\n\n\n\u003Cli>Peeking. This does not correct for checking results every day and stopping when you like the number. Bayesian chance-to-beat is easier to live with than p-values in that setting, but it is not a licence to ignore a sequential testing plan.\u003C\u002Fli>\n\n\n\n\u003Cli>Two variations only. Multivariate or bandits need a different tool.\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"when-this-is-the-right-tool\">When this is the right tool\u003C\u002Fh2>\n\n\n\n\u003Cp>Use it to debrief a live or finished A\u002FB test: paste the counts, set a winner threshold your team actually uses, and read chance-to-beat together with expected loss. Use Share results when you want a colleague to see the same inputs.\u003C\u002Fp>\n\n\n\n\u003Cp>If you need “how many visitors until 80% power at 20% MDE”, that is a frequentist sample-size question — not this calculator.\u003C\u002Fp>\n\n\n\n\u003Cp>If you want a hand interpreting a messy test (unequal split, multiple metrics, or a rollout decision), \u003Ca rel=\"noreferrer noopener\" href=\"\u002Fget-in-touch\u002F\" data-type=\"page\" data-id=\"449\">get in touch\u003C\u002Fa>.\u003C\u002Fp>\n",[],[9],108,[11,12,13,14,15,16],"post-39934","tools","type-tools","status-publish","has-post-thumbnail","hentry",{"gated":18,"form_title":19,"form_button_text":20,"confirmation_title":21,"confirmation_body":22,"tool_link":22,"page_content_modules":23,"hero":183},false,"Access Tool","Get Access","Thank you","",[24,152],{"acf_fc_layout":25,"reduced":26,"top_overlap":-1,"bottom_underlap":-1,"disable_observer":26,"title":27,"content":6,"authors":28,"gated":18,"form_title":19,"form_button_text":20,"confirmation_title":21,"confirmation_body":22,"tool_link":22},"tool_content",true,"Bayesian AB Test CRO Calculator",[29],{"id":30,"path":31,"display_name":32,"job_title":33,"description":34,"avatar_url":35,"acf":36},1,"\u002Fabout\u002Four-team\u002Faaron-dicks\u002F","Aaron Dicks","Chief Technology Officer","Chief Technology Officer of Impression. 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