Margin of Error Calculator

Calculate the margin of error for your survey results based on sample size, confidence level, population size, and expected proportion.

Inputs

Sample Size

Confidence Level

Population Size

Expected Proportion (%)

Tab to move between fields

Results

Margin of Error

Confidence Interval Lower45%

Confidence Interval Upper55%

Z-Score Used1.96

Finite Population Correction1

How to Use This Calculator

Enter the sample size — the number of completed survey responses you have or plan to collect.
Select the confidence level: 1 for 90%, 2 for 95%, or 3 for 99%.
Enter the population size if known, or leave at 0 to treat the population as effectively infinite.
Input the expected proportion or use 50% (most conservative) if unknown.
Review the Margin of Error, Confidence Interval bounds, Z-Score used, and Finite Population Correction factor.
Adjust the sample size or confidence level and compare results on the Margin of Error by Sample Size chart.

How the result changes with Sample Size

Sample Size	Margin of Error
100,001	0.31%
350,001	0.17%
650,000	0.12%
900,000	0.1%

What each input means

Sample Size: The number of completed survey responses you have or plan to collect.
Confidence Level: Select 1 for 90%, 2 for 95%, or 3 for 99%. Higher confidence requires a larger margin of error.
Population Size: Total size of the population being studied. Leave at 0 for infinite/unknown population.
Expected Proportion (%): The expected proportion answering a certain way. Use 50% for maximum variability (most conservative estimate).

How this is calculated

Worked example, using the default values

Identify Input Parameters
4 parameters
Sample Size = 384, Confidence Level = 2, Population Size = 0, Expected Proportion (%) = 50 = 4 input(s) provided
Calculate Margin of Error
Margin of Error = m
5 = 5
Calculate Confidence Interval Lower
Confidence Interval Lower
45 = 45
Calculate Confidence Interval Upper
Confidence Interval Upper
55 = 55

Engine last updated August 6, 2026. Checked against 2 independently-derived tests — how we verify calculators.

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