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Sample Size Calculator

Calculate the minimum number of survey respondents needed for statistically valid results. Includes finite population correction for small communities.

Quick Answer
Standard survey (95% CI, ±5% MoE): n = 385. National poll (95%, ±3%): n = 1,068. Population size barely matters — 400 responses work equally well for 1M or 300M populations.

Typical surveys: 3–5%

Use 50% for maximum sample size

Confidence / Margin of Error Required Sample Size Typical Use Who Uses This
95% / ±5% 385Standard surveyAcademic research, company surveys
95% / ±3% 1,068National pollYouGov UK, Gallup US, Morgan Stanley AU
95% / ±2% 2,401High-accuracy pollMajor election polls
99% / ±5% 664High-confidence surveyMedical surveys, FDA studies
99% / ±1% 16,588Very precise estimateGovernment census supplements
90% / ±5% 271Exploratory researchBusiness pilots, quick polls

Frequently Asked Questions

Why does population size barely affect the required sample size?

For large populations (N > 10,000), the sample size formula n = (z²pq)/e² is independent of population size. This is counterintuitive but correct: accuracy depends on the absolute sample size, not the proportion of the population sampled. A sample of 400 gives ±5% accuracy whether the population is 10,000 or 1 billion. The finite population correction only makes a meaningful difference when you're sampling more than ~10% of the total population.

How do UK and US polling companies determine sample size?

YouGov UK typically uses weighted samples of 1,000–2,000 for national surveys (targeting ±3% MoE at 95% CI). Ipsos UK and MORI use similar sizes. US Gallup polls use 1,000–1,500 adults. Australian Essential Media uses 1,000 respondents. All target 95% confidence. The difference from a strict statistical sample is that all these organisations use quota sampling and weighting — not purely random sampling — which affects the effective sample size.