Sample Size
3 min read
Definition
Sample size is the number of respondents you need to survey in order to get results that accurately reflect the larger population. Too small a sample produces unreliable results; too large a sample wastes resources. The ideal sample size depends on your population size, desired margin of error, and confidence level.
How to Calculate
n = (Z^2 x p x (1-p)) / e^2
n = required sample size
Z = z-score (1.96 for 95% confidence)
p = expected proportion (0.5 for max variability)
e = desired margin of error (e.g., 0.05 for +/-5%)
For finite populations, apply the finite population correction factor: n_adj = n / (1 + (n-1)/N), where N is the population size.
Quick Reference Table
At 95% confidence level:
| Population | MoE +/-5% | MoE +/-3% |
|---|---|---|
| 500 | 217 | 340 |
| 1,000 | 278 | 516 |
| 5,000 | 357 | 879 |
| 10,000 | 370 | 964 |
| 100,000 | 383 | 1,056 |
| 1,000,000+ | 384 | 1,067 |
Why It Matters for Surveys
- Statistical validity: an adequate sample size ensures your conclusions are trustworthy.
- Resource planning: know how many responses you need before launching a campaign.
- Survey design: if you need 1,000 responses and your expected response rate is 25%, you need to invite 4,000 people.
- Subgroup analysis: if you plan to segment results, each subgroup needs a sufficient sample.
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