How many people do you need to survey?
Set your confidence level and how much error you can live with, then see the exact number of responses your survey needs, plus how the sample grows as you tighten the margin and how many invites to send at your response rate.
Your survey
Leave large or blank to treat the population as effectively infinite. The correction only bites for small groups.
Used to work out how many invites to send so enough people actually reply.
What your number means
Smaller margin, much bigger sample
Required sample size vs margin of error
Sample size scorecard
Required responses by margin of error
At your chosen confidence and proportion, with the finite population correction applied where a population size is set.
| Margin of error | Base sample | With population | Invites to send |
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Sample size, explained
Why Sample Size Matters
Sample size directly affects the reliability of your results. Too small a sample leads to unreliable conclusions with wide confidence intervals. Too large wastes resources. Calculating the right sample size before collecting data ensures your study has enough statistical power to detect meaningful differences.
How Sample Size Is Calculated
The formula uses four inputs: confidence level (z-score), margin of error, population proportion, and population size. For large populations, n = (z² × p × (1-p)) / e², where z is the z-score, p is the proportion, and e is the margin of error. A finite population correction is applied for smaller populations.
Choosing Your Parameters
A 95% confidence level and 5% margin of error are standard for most surveys. If you do not know the expected proportion, use 50%, this maximizes the required sample size, ensuring your estimate is conservative. Lower margins of error require larger samples.
Sample Size for Common Scenarios
For a national survey with 95% confidence and ±3% margin, you need about 1,067 respondents. For a small town of 5,000 with the same parameters, about 880. As population grows beyond ~20,000, the required sample size barely changes, you approach the infinite population formula.
Common questions
Why is 50% used as the default population proportion?
Using 50% gives the most conservative (largest) sample size estimate. When p = 0.5, the product p(1-p) is maximized at 0.25. If the actual proportion is different, you will have more than enough responses.
Does population size matter for large populations?
For populations above ~20,000, the required sample size barely changes. The finite population correction only makes a significant difference for small populations (under a few thousand).
How can I reduce the required sample size?
Accept a wider margin of error, use a lower confidence level, or use a more specific population proportion estimate (if justified by prior research). Each of these reduces the required n.
What is the minimum viable sample size?
As a general rule, at least 30 observations are needed for the Central Limit Theorem to apply. For surveys, most methodologists recommend a minimum of 100 respondents, with 384 being a common target for 95% confidence and ±5% margin.
Estimates for planning only. These formulas assume simple random sampling from the population. Cluster, stratified or weighted designs, and non-response bias can change how many responses you truly need. Treat the result as a solid starting target, not a guarantee.