Sample size calculator (survey)
Find out how many people to survey based on margin of error and confidence level.
Use 50% when you don't know the expected answer (worst case, needs the largest sample). Enter the population only if it is small (e.g., students in a school).
How the calculation works
The base sample is n₀ = Z²·p·(1−p) ÷ E², where Z depends on the confidence (1.96 for 95%), p is the expected proportion and E the margin of error. For small populations, apply the correction n = n₀ ÷ (1 + (n₀−1)/N).
Examples
- 95% confidence, 5% margin, 50% proportion: ~384 responses (large population).
- For a 3% margin instead of 5%, the sample nearly triples (~1,068).
Frequently asked questions
What is the margin of error?
It is how much the result may vary up or down. E.g., 60% ± 5% means between 55% and 65%.
What proportion should I assume if unknown?
Use 50% — the most conservative case, requiring the largest sample.
Is a bigger sample always better?
It reduces error, but with diminishing returns and higher cost. Doubling precision needs ~4× the sample.
How many people do I need to survey?
For a large population, 385 responses give ±5% at 95% confidence; 1,068 give ±3% and 2,401 give ±2%.
Does population size matter?
Very little. A city of 100,000 and a country of 200 million need nearly the same sample size.
Why is a smaller margin of error so expensive?
Because sample size grows with the square of precision: going from ±2% to ±1% quadruples the interviews.
Does a large sample guarantee a good survey?
No. The formula assumes random sampling and cannot fix selection or non-response bias. A biased sample of 10,000 is worse than a random one of 400.
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Updated on June 30, 2026 · by Rafael Rossi · Methodology & sources