[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"cms-page:\u002Fresources\u002Ftools\u002Fsample-size-calculator":3},{"date":4,"date_gmt":4,"content":5,"categories":7,"coauthors":8,"class_list":10,"acf":17,"head_links":185,"breadcrumbs":186,"head":196},"2026-09-19T20:35:57",{"rendered":6},"\u003Cp>[calculator-sample-size]\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"how-to-read-this-bayesian-sample-size-planner\">How to read this Bayesian sample-size planner\u003C\u002Fh2>\n\n\n\n\u003Cp>Use this before you launch (or when you are deciding whether a test is even worth running). You assume a baseline conversion rate and a true effect size for B vs A, then ask: under our chance-to-beat decision rule, how many visitors do simulations usually need before a winner is called?\u003C\u002Fp>\n\n\n\n\u003Cp>It is a planning companion to the Bayesian A\u002FB results calculator — not a frequentist power calculator, and not a guarantee of the sample size your live test will need. An example with the default assumptions is shown immediately; change the inputs and press Update results when you want a fresh plan.\u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"what-to-enter\">What to enter\u003C\u002Fh3>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>Baseline conversion rate (A) — your expected control rate (e.g. 10%).\u003C\u002Fli>\n\n\n\n\u003Cli>Effect size — relative % (B is 20% higher than A) or absolute percentage points (B is +2pp). The line under the field shows the other unit so you can sanity-check the assumption.\u003C\u002Fli>\n\n\n\n\u003Cli>Traffic to A — default 50\u002F50; change it if you will run an unequal split.\u003C\u002Fli>\n\n\n\n\u003Cli>Winner threshold — the bar for naming a winner (90%, 95% or 99%). Same language as the results calculator.\u003C\u002Fli>\n\n\n\n\u003Cli>Max expected loss (optional) — if set, a simulation only “decides” when chance-to-beat clears the threshold and expected loss on the leading arm is under this ceiling.\u003C\u002Fli>\n\n\n\n\u003Cli>Max total visitors — the cap for each simulated test. If many simulations are still undecided at the cap, raise this or lower the threshold.\u003C\u002Fli>\n\n\n\n\u003Cli>Weekly visitors (optional) — turns the median into calendar weeks, and always shows the visitors\u002Fweek needed to hit the median in about four weeks.\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>Share results copies a link with those inputs (not a frozen median), so anyone opening it gets the same assumptions and a fresh simulation.\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>Median visitors to a decision is the middle of many simulated tests under your assumed true effect — including runs that never decide and sit at the visitor cap. It is not a promised sample size for your next experiment.\u003C\u002Fp>\n\n\n\n\u003Cp>The p10–p90 range is the typical spread of stopping times. A wide range means the answer is sensitive to noise even when the true lift is fixed.\u003C\u002Fp>\n\n\n\n\u003Cp>Illustrative counts at the median convert that total into approximate visitors and conversions per arm if rates match your assumption. Handy for briefing, not a forecast of what you will observe.\u003C\u002Fp>\n\n\n\n\u003Cp>Sensitivity (half \u002F stated \u002F 1.5× effect) shows how the median moves if the true lift is smaller or larger than you stated. If half-effect needs far more traffic than you can afford, rethink the test or the effect you are designing for.\u003C\u002Fp>\n\n\n\n\u003Cp>Correct-call rate (among decided simulations) is how often the planner picked the truly better variation when a decision was made under the assumed lift. It is not the same as frequentist power.\u003C\u002Fp>\n\n\n\n\u003Cp>Null false-positive rate is the share of zero-lift simulations that still called a winner by the cap. Each simulation is one test that grows over time; checking chance-to-beat along that path inflates this — treat it as a caution about peeking, not a Type I error guarantee.\u003C\u002Fp>\n\n\n\n\u003Cp>The stopping-time chart shows where simulations decided. The last bar is the share still undecided at max visitors.\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>Defaults: 10% baseline, +20% relative lift for B (≈ +2pp), 50\u002F50 traffic, 95% winner threshold, 50,000 max visitors.\u003C\u002Fp>\n\n\n\n\u003Cp>You should see a modest median (often in the low thousands in these simulations), a sensitivity strip where half-effect needs more traffic than the stated effect, a high correct-call rate among decided runs, and a material null false-positive rate — because sequential checks make “false wins” more common than a single end-of-test look.\u003C\u002Fp>\n\n\n\n\u003Cp>Try absolute mode with +2pp instead of +20% relative: near-equivalent assumptions should produce a similar planning picture. Add weekly visitors if you want the calendar framing.\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>Assumed effect, not observed data. Optimistic lifts produce optimistic sample sizes. Prefer an effect you would actually ship for, not a stretch goal.\u003C\u002Fli>\n\n\n\n\u003Cli>Not frequentist power \u002F MDE. If your stakeholder asks for “80% power at 5% significance”, this tool answers a different question. Say so up front.\u003C\u002Fli>\n\n\n\n\u003Cli>Peeking. The null false-positive callout exists because the planner checks along the path. Live peeking without a sequential plan has the same problem.\u003C\u002Fli>\n\n\n\n\u003Cli>Cap pile-up. If undecided share is high, the median understates how often you would still be waiting. Follow the on-screen cap advice.\u003C\u002Fli>\n\n\n\n\u003Cli>Two variations only. Multivariate or bandits need a different design.\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"when-this-is-the-right-tool\">When this is the right tool\u003C\u002Fh3>\n\n\n\n\u003Cp>Use it to plan a Bayesian A\u002FB test you intend to decide with chance-to-beat (and optionally expected loss): set a believable baseline and effect, pick the threshold your team uses, and read median plus honesty metrics together.\u003C\u002Fp>\n\n\n\n\u003Cp>When the test is live or finished, paste the real counts into the Bayesian A\u002FB results calculator instead.\u003C\u002Fp>\n\n\n\n\u003Cp>If you want a hand sizing a messy programme (multiple metrics, uneven traffic, or a hard calendar constraint), \u003Ca rel=\"noreferrer noopener\" href=\":8443\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-39937","tools","type-tools","status-publish","has-post-thumbnail","hentry",{"gated":18,"page_content_modules":19,"hero":180},false,[20,149],{"acf_fc_layout":21,"reduced":22,"top_overlap":-1,"bottom_underlap":-1,"disable_observer":22,"title":23,"content":6,"authors":24,"gated":18,"form_title":-1,"form_button_text":-1,"confirmation_title":-1,"confirmation_body":-1,"tool_link":-1},"tool_content",true,"Sample size calculator",[25],{"id":26,"path":27,"display_name":28,"job_title":29,"description":30,"avatar_url":31,"acf":32},1,"\u002Fabout\u002Four-team\u002Faaron-dicks\u002F","Aaron Dicks","Chief Technology Officer","Chief Technology Officer of Impression. 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