Sample Size Calculator

Enter your population size, confidence level, and margin of error to calculate the required sample size for your survey or study. The Sample Size Calculator also shows you the margin of error for any existing sample, so you can validate whether your data meets statistical standards.

The total number of people whose opinion or behavior your sample will represent.

The probability that your sample accurately reflects the attitudes of your population. Industry standard is 95%.

%

The range (as a percentage) that your population's responses may deviate from your sample's. Common values are 1%–10%.

%

The estimated proportion of your population that will choose a given answer. Use 50% if unknown — this gives the most conservative (largest) sample size.

Results

Required Sample Size

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Actual Margin of Error

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Z-Score Used

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Sample as % of Population

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Sample vs. Remaining Population

Frequently Asked Questions

What is sample size?

Sample size is the number of individuals selected from a population to participate in a survey or study. A properly calculated sample size ensures your results are statistically representative of the broader population, without needing to survey everyone.

What is a good sample size?

A good sample size is one that achieves your desired confidence level and margin of error. For most surveys, a margin of error of ±5% with a 95% confidence level is considered the industry standard. Larger populations typically require larger samples, but the returns diminish quickly — a sample of ~385 is sufficient for nearly any population at 95% confidence and ±5% margin of error.

What is a large sample size?

There's no universal definition, but samples over 1,000 are generally considered large in survey research. Large samples reduce the margin of error and increase statistical power, but they also increase cost and time. Beyond a certain point, increasing sample size yields diminishing improvements in accuracy.

How is sample size calculated?

The formula uses your confidence level (converted to a Z-score), margin of error, and estimated response distribution (p). For an infinite population: n = Z² × p × (1−p) / e². For a finite population, a correction factor is applied: n_adjusted = n / (1 + (n−1)/N), where N is the population size.

What is a Z-score and why does it matter?

A Z-score represents the number of standard deviations from the mean corresponding to your chosen confidence level. For a 95% confidence level, the Z-score is 1.96; for 99% it is 2.576. A higher Z-score reflects greater certainty but requires a larger sample size.

What is margin of error and how does it affect sample size?

Margin of error is the maximum expected difference between your sample results and the true population value. A smaller margin of error (e.g. ±1%) requires a much larger sample than a wider margin (e.g. ±5%). Halving the margin of error roughly quadruples the required sample size.

What is response distribution and what value should I use?

Response distribution (also called population proportion) is the estimated percentage of respondents who will select a particular answer. If you don't know this value, use 50% — it produces the largest (most conservative) sample size estimate and ensures your sample is sufficient regardless of actual responses.

What are common sample size mistakes to avoid?

Common mistakes include using too wide a margin of error (which makes results unreliable), assuming a small sample is fine for a large population, ignoring non-response rates (always collect more than the minimum), and forgetting to apply the finite population correction when your population is small.

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