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Sample Size Calculator

Estimate a proportion sample size from confidence, margin of error, expected proportion and finite population.

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Complete guide

Sample size calculator for a population proportion

Choose confidence, margin of error, expected proportion and optional population size. Calculator24 returns the minimum completed sample, unadjusted size, finite-population correction and nonresponse scenarios.

Editorial checkNormal-approximation and finite-population formulas tested at 90%, 95% and 99% confidence.Updated September 10, 2026
Precision costs sample sizeHalving margin of error requires about four times the sample before finite-population correction.1Confidence2Margin of error3Required completed sample
Precision costs sample sizeHalving margin of error requires about four times the sample before finite-population correction.

What this sample size estimates

The result plans estimation of one population proportion using a two-sided normal approximation and simple random sampling.

Core proportion formula

The most conservative planning value is p=0.5 because p(1−p) is largest there.

n₀=z²p(1−p)/e²

Finite population correction

When sampling a meaningful fraction of a known finite population, the correction reduces the required completed sample.

n=n₀/[1+(n₀−1)/N]

Worked 95% example

With z≈1.96, p=0.5 and e=0.05, n₀≈384.15, which rounds upward to 385 completed responses.

Design effect and nonresponse

A design effect inflates the effective sample for clustering or unequal weights. Nonresponse scenarios show how many invitations may be needed but are not included in the primary completed-sample result.

When this formula is insufficient

Small expected counts, complex surveys, paired designs, multiple primary outcomes and hypothesis tests need a design-specific power analysis.

Common two-sided normal critical values

Critical values used by this planning calculator.
Confidencez critical
90%1.64485
95%1.95996
99%2.57583

Frequently asked questions

Why is 50% the default proportion?

It produces the largest variance and a conservative sample when the true proportion is unknown.

Should sample size be rounded up?

Yes. A partial respondent cannot satisfy the target.

What does population zero mean?

The population is treated as very large, so no finite-population correction is applied.

Does this include nonresponse?

The main result is completed responses; invitation scenarios are shown separately.

What is design effect?

An inflation factor for variance from complex sampling relative to simple random sampling.

Is this a statistical power calculator?

No. It plans precision for one proportion.

What to keep in mind

This planning formula targets one proportion under a normal approximation and completed simple-random responses. Add expected nonresponse separately; clustering, weighting, subgroup estimates and rare outcomes need study-specific design.

Results display up to 8 decimal places; exported numbers preserve calculation precision. This is a calculation summary, not an official certificate.

Sources and references

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