Sample Size Calculator for Surveys & Experiments
Calculate the sample size you need for a statistically valid survey or experiment, based on confidence level, margin of error, and population size.
How This Sample Size Calculator Works
Before running a survey or experiment, you need to know how many responses or observations are actually enough to draw a statistically meaningful conclusion, too few and your results could be dominated by random chance, too many and you’re wasting time and budget collecting data you didn’t need. This tool uses the standard Cochran formula, the same one used in most introductory statistics courses and survey methodology guides, to calculate that minimum sample size.
Three inputs drive the calculation: your desired confidence level (how sure you want to be that your sample reflects the true population, commonly 95%), your acceptable margin of error (how much your sample result might differ from the true value, commonly ±5%), and your expected proportion (your best guess at the split you’re measuring, defaulting to the conservative 50/50 assumption when you have no prior information).
If you also know your total population size, for example, you’re surveying a company of 3,000 employees rather than the general public, enter it, and the tool applies a finite population correction that reduces the required sample size, since a smaller total population needs proportionally fewer respondents to represent it accurately.
All of this is standard, published statistical methodology, computed instantly and precisely in your browser using the confidence level’s corresponding Z-score from the standard normal distribution.
What this tool does
How to use it
Choose your confidence level
Select how certain you want to be that your sample represents the true population, 95% is the most common choice.
Set your margin of error
Decide how much sampling error you’re willing to accept, smaller margins require larger samples.
Enter your expected proportion
Use 50% if you have no prior estimate, since it produces the most conservative (largest) required sample size.
Add population size if known
If you’re sampling from a specific, finite group, enter its size for a more precise, often smaller, required sample.
FAQ: Sample Size Calculator
95% is the most widely used standard in survey research and academic publishing, striking a reasonable balance between certainty and practical sample size. Use 99% for higher-stakes decisions where being wrong is costly, or 90% when a rougher estimate is acceptable and you want a smaller, cheaper sample.
Margin of error describes the range around your sample result that likely contains the true population value, a result of ‘60% ± 5%’ at 95% confidence means you’re 95% confident the true population value falls between 55% and 65%. Smaller margins of error require larger sample sizes, since you’re demanding more precision.
The term p(1−p) in the sample size formula is maximized when p = 0.5 (50%), which produces the largest, most conservative required sample size. Using 50% when you don’t have a solid prior estimate protects you from under-sampling, since any other true proportion would actually require a smaller sample than what you calculated.
Finite population correction adjusts the required sample size downward when you’re sampling from a known, limited population rather than an effectively infinite one, since a smaller total group needs proportionally fewer respondents to represent it well. Use it whenever you know your total population size and it’s relatively small, for example, surveying employees at a specific company rather than the general public.
A commonly cited reference figure is about 385 respondents for 95% confidence, a 5% margin of error, and the conservative 50% proportion assumption, applied to an effectively unlimited population. This number appears frequently in research methodology guides precisely because those are such common default assumptions.
No, and this is a common misconception, once a population is reasonably large (several thousand or more), the required sample size levels off and barely increases further, because the finite population correction’s effect diminishes as population size grows large relative to the sample. A survey of 100,000 people and a survey of 10,000,000 people often need nearly the same sample size for the same confidence level and margin of error.