How to use
- Choose the planning mode that matches your decision: one proportion, one mean, or a balanced two-proportion comparison.
- Enter a confidence level and the largest margin of error you can tolerate.
- Use a realistic planning rate or sigma, then round up and treat the result as the minimum sample to collect.
Wave 2 statistics expansion
Three sample-size planning workflows
Use this when you need a survey-style sample size for one rate or share, such as approval, defect, or conversion prevalence.
Inputs
Plan a survey or audit when the output is one proportion such as approval, defect, prevalence, or response share.
Plan one mean estimate when the output is an average in real units, such as wait time, temperature, score, or cost.
Plan balanced A/B groups when you want the estimated difference in rates to land within a target margin at a chosen confidence level.
This first release assumes balanced group sizes and focuses on confidence interval width, not power or minimum detectable effect.
What the page computes
- The selected mode sets the planning formula for one proportion, one mean, or two balanced proportions.
- The page uses the chosen confidence level to compute the matching normal critical value.
- The required sample size is rounded up so the reported precision target is met or slightly exceeded.
How to choose the right mode
- Use one proportion for survey shares, approval rates, defect rates, and similar single-rate estimates.
- Use one mean when the output is an average in real units such as minutes, dollars, kilograms, or exam score points.
- Use two proportions when you want a balanced A/B estimate of the gap between two rates and you care about confidence interval width, not power.
- If the actual planning question is “How likely are we to detect a minimum effect?”, move to power analysis instead of staying on this page.
- If the next question is whether the observed result is significant, move from planning to an analysis page such as t-test, chi-square test, or CI & hypothesis tests.
Survey vs experiment
Survey sizing and A/B sizing often sound similar, but they solve different planning problems. Survey mode estimates one rate such as approval or prevalence. Two-proportion mode estimates the gap between two rates under balanced groups. Neither mode on this page answers the power-analysis question of how likely you are to detect a minimum effect under a test.
If your stakeholders ask, “How many responses do we need so the estimate is within ±3 points?”, this page is the right start. If they ask, “How many users do we need to detect a +2 point lift with 80% power?”, that is a power-analysis problem and should be handled separately.
What finite population correction means
Finite population correction matters when your sample is a meaningful fraction of the full population. For example, auditing 180 records out of 400 is different from surveying 180 people out of a city of millions. In the small-population case, each observed unit removes more uncertainty, so the required sample can be smaller than the infinite-population formula suggests.
Turn finite population correction on for surveys, audits, and inventories with a known bounded population. Leave it off for large populations, streams of future traffic, or A/B tests where the practical population is not fixed in the same way.
What to do after sizing
After collection, move to a confidence interval or chi-square workflow if you want inference instead of planning.
- T-Test CalculatorUse this next when your collected outcome is a mean and you need a confidence interval or test.
- Chi-Square Test CalculatorUse this next for contingency tables and count-based comparisons after data collection.
- Confidence Interval & Hypothesis Test WizardUse this next when you want broader inference workflows for means and proportions.
- Power Analysis CalculatorUse this instead when the planning question is required sample size for a target power or minimum detectable effect.
- Statistics (inference & tests)Return to the topic hub for neighboring regression, distribution, and inference pages.
FAQ
When should I use this page instead of a power analysis tool?
Use this page when your goal is precision: you want a confidence interval or rate estimate to stay within a chosen margin of error. Use a power analysis tool when the goal is to detect a minimum effect with a specified power.
Why does 50% produce the largest survey sample size?
For a single proportion, variance is largest near p = 0.50. That makes the required sample size conservative. If you have prior evidence that the true rate is farther from 50%, the required sample can be smaller.
What does finite population correction change?
Finite population correction reduces the required sample when you are drawing from a limited population and your planned sample is a noticeable fraction of that population. It matters most for surveys or audits of small populations.
Does the A/B mode calculate power?
No. The A/B mode on this page plans a balanced two-proportion sample size for a target confidence interval width. It does not include hypothesis-test power or minimum detectable effect workflows.
Does the share URL include my inputs?
No. The share URL stores only lightweight settings such as mode, confidence level, and whether finite population correction is enabled. Entered numeric values stay in your browser.
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