Sample Size Calculator
Calculate the required sample size for a survey or experiment given confidence level, margin of error, and population size.
95% is standard for most research
+/- this percentage (5% is common)
Total number of people in the group you are studying. Leave large (100,000+) if unknown.
Expected response rate or split. Use 50% if unknown (maximises sample).
How It Works
Uses Cochran's formula: n = (Z^2 * p * (1-p)) / E^2, adjusted for finite populations using n_adj = n / (1 + (n-1)/N). Z is the z-score for the confidence level (1.96 for 95%), p is the expected proportion, and E is the margin of error.
Frequently Asked Questions
What sample size do I need for a 95% confidence level?
For a 95% confidence level with +/-5% margin of error and 50% proportion: population of 1,000 needs 278 responses; 10,000 needs 370; 100,000 needs 383; 1 million+ needs 385. Larger populations barely change the required sample.
What confidence level should I use?
95% is the standard for most research and surveys. 90% is acceptable for preliminary research or quick polls. 99% is used in medical, pharmaceutical, and high-stakes research where errors are costly.
Does a larger sample always mean better results?
Yes, up to a point. A larger sample reduces the margin of error and increases precision. However, the benefit of each additional respondent diminishes — going from 100 to 200 respondents has a bigger impact than going from 1,000 to 1,100.
What is the margin of error?
The margin of error represents the range within which the true population value is expected to fall. If 60% of your sample says 'yes' with a +/-5% margin, the true value is likely between 55% and 65%.
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