Sample Size Calculator
Sample Size Calculator
Enter your desired confidence level, margin of error, and (if you have an estimate) the expected population proportion. This calculates the minimum sample size needed so your survey or study results are precise enough at that confidence level. If you know your total population size (e.g. a company with 2,000 employees), enter it too for a more accurate, finite-population-corrected result.
- Formula: n = (z² × p × (1 − p)) ÷ e², where z is the critical value for your confidence level, p is the estimated proportion, and e is your target margin of error.
- 50% proportion is the safe default: if you have no prior estimate of the proportion you're measuring, 0.5 gives the most conservative (largest) required sample size, guaranteeing your margin of error target regardless of the true proportion.
- Finite population correction: for smaller, known populations, entering the population size reduces the required sample size, since you can't sample more people than exist.
Why does a smaller margin of error require a much larger sample?
Required sample size grows with the square of how precise you want to be — halving your margin of error (say from 5% to 2.5%) roughly quadruples the sample size needed, not just doubles it.
What if I don't know the population proportion in advance?
Leave it at the default of 0.5 (50%) — this is the value that produces the largest, most conservative required sample size, so your result stays valid even if the true proportion turns out to be quite different.
Sample Size Calculator
Enter your desired confidence level, margin of error, and (if you have an estimate) the expected population proportion. This calculates the minimum sample size needed so your survey or study results are precise enough at that confidence level. If you know your total population size (e.g. a company with 2,000 employees), enter it too for a more accurate, finite-population-corrected result.
- Formula: n = (z² × p × (1 − p)) ÷ e², where z is the critical value for your confidence level, p is the estimated proportion, and e is your target margin of error.
- 50% proportion is the safe default: if you have no prior estimate of the proportion you're measuring, 0.5 gives the most conservative (largest) required sample size, guaranteeing your margin of error target regardless of the true proportion.
- Finite population correction: for smaller, known populations, entering the population size reduces the required sample size, since you can't sample more people than exist.
Why does a smaller margin of error require a much larger sample?
Required sample size grows with the square of how precise you want to be — halving your margin of error (say from 5% to 2.5%) roughly quadruples the sample size needed, not just doubles it.
What if I don't know the population proportion in advance?
Leave it at the default of 0.5 (50%) — this is the value that produces the largest, most conservative required sample size, so your result stays valid even if the true proportion turns out to be quite different.