Sample Size Calculator for Proportions
Determine how many observations you need to estimate a binomial proportion with your desired confidence interval width. Set the expected proportion, confidence level, and allowable margin of error, and choose the confidence interval method: normal approximation (Wald), Wilson, or exact (Clopper-Pearson).
Typical use cases
- Estimating response rates in surveys or opt-in campaigns
- Planning quality checks where pass/fail outcomes are recorded
- Sizing medical prevalence studies or conversion benchmarks
Inputs you control
- Target confidence level
- Expected proportion (p)
- Margin of error you want to achieve
- Confidence interval method (Wald, Wilson, or exact)
- Optional population size for finite population correction