Sample Size Calculator

Sample size is the number of individuals or observations included in a study or survey. It represents the subset of a larger population that you collect data from. A correctly calculated sample size ensures your results are statistically representative of the full population without needing to survey everyone.

A larger sample size reduces the margin of error and increases the reliability of your results. Too small a sample can lead to inaccurate conclusions, while an unnecessarily large sample wastes time and resources. The ideal sample size balances statistical confidence with practical feasibility.

The confidence level represents how certain you want to be that your sample results reflect the true population. A 95% confidence level — the most common standard — means that if you ran the same survey 100 times, 95 of those surveys would produce results within your stated margin of error. Use 99% for critical research and 90% for less formal studies.

The margin of error (also called the confidence interval) is the range of values above and below your survey result within which the true population value is expected to fall. For example, a result of 60% with a ±5% margin of error means the true value is likely between 55% and 65%. A smaller margin of error requires a larger sample size.

A z-score is a statistical value that corresponds to your chosen confidence level. It represents the number of standard deviations a data point is from the mean of a normal distribution. For a 95% confidence level the z-score is 1.96, for 99% it is 2.576, and for 90% it is 1.645. The z-score is used directly in the sample size formula.

If you have no prior data on how your population will respond, use 50%. This is the most conservative estimate and produces the largest (safest) sample size, ensuring your results will be statistically valid regardless of the actual distribution. If you have historical data, use that proportion instead to potentially reduce your required sample size.

When your population is very large or effectively unlimited (e.g. all internet users), enter a large number like 1,000,000 or more. For very large populations, the required sample size converges to a fixed value determined mainly by your confidence level and margin of error, and becomes insensitive to the exact population figure.

This calculator is designed for surveys and opinion polls — it finds the sample needed to estimate a population proportion within a margin of error at a given confidence level. An A/B test calculator (used in conversion rate optimization) accounts for a baseline conversion rate, minimum detectable effect, and statistical power, and calculates the number of visitors needed per variation in an experiment.