How many survey responses do you actually need?

What does margin of error mean?

Margin of error is the range around your survey result that the true population value is likely to fall within. A 5% margin of error on a result of 60% means the true figure is likely between 55% and 65%.

Why use 50% as the default proportion estimate?

The term p×(1−p) in the sample size formula is maximized when p=50%, so using 50% as your estimate produces the largest, most conservative required sample size. If you have a genuine prior estimate of the proportion (say, from a pilot survey), using it instead can lower the required sample size.

What is the finite population correction, and when do I need it?

It adjusts your required sample size downward when your sample would make up a meaningful fraction of a small, known total population — surveying 500 people out of a population of 600 needs a smaller correction-adjusted sample than the base formula suggests. For large or unknown populations, the correction has negligible effect, which is why the population size field is optional.

Does higher confidence always mean I need a bigger sample?

Yes. Higher confidence levels use a larger z-value (1.645 for 90%, 1.96 for 95%, 2.576 for 99%), and since the required sample size scales with the square of the z-value, moving from 95% to 99% confidence noticeably increases the sample you need for the same margin of error.

Worked example: at 95% confidence (z=1.96), a 5% margin of error, and the conservative 50% proportion estimate: n = (1.96² × 0.5 × 0.5) / 0.05² = 0.9604/0.0025 ≈ 384.16, rounded up to 385 — the classic "385 respondents" figure widely cited for a 95%/±5% survey with no known population size.