Choose a mean or proportion, confidence level, sample size, and sample information to calculate the margin of error.
Calculate margin of error with mean or proportion modes, finite-population correction, interval bounds and sample-size planning evidence.
Use descriptive context first, choose the method that matches the design, then keep inputs, settings and result evidence reproducible instead of copying a p-value without its analysis recipe.
Use plots and descriptive summaries before formal tests. Check the sampling/design assumptions that matter for the chosen method, report effect size and uncertainty where available, and avoid treating a threshold such as p < .05 as proof of importance, causation, or truth. Browser calculations are educational/planning utilities, not domain-specific professional advice.
Use the tool first, then use these checks to interpret, verify and hand off the result without displacing the primary workflow.
A mean with an estimated standard deviation and a proportion with binomial uncertainty use different standard-error models. Confirm the selected mode, confidence level, sample size, and input meaning before interpreting the margin as if every survey or experiment used one universal formula.
FPC can reduce uncertainty when sampling without replacement from a known finite population and the sample is a meaningful fraction of that population. Do not apply it automatically to open-ended populations, repeated processes, or designs where the sampling mechanism does not match the simple model.
Margin of error under a statistical model does not include nonresponse bias, coverage error, question wording, measurement error, weighting problems, or a nonrandom sample. Report those limitations separately and avoid presenting a narrow confidence interval as proof that the entire study is accurate.
A 50% proportion is often conservative for simple random sampling, but clustered or weighted designs can require more observations. Use the calculator as a baseline, then verify the design assumptions and anticipated response rate before turning the result into a recruitment target.
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