Sample Size Calculator
Determine how many participants you need for your survey or quantitative study to achieve your desired level of statistical precision.
Planning a Hypothesis Test?
If you need to know which statistical test to run on your collected data, use our interactive test selector.
Use Statistical Test Selector ?Confused by the terminology?
Explore clear definitions of concepts like margin of error, confidence interval, and population.
Visit Research Glossary ?Understanding Sample Size
What is Sample Size?
In quantitative research, sample size refers to the number of subjects or observations included in a study. Because it is rarely feasible to collect data from an entire population, researchers collect data from a smaller subset (a sample) and use statistical methods to make inferences about the larger group.
Why Does It Matter?
Choosing the correct sample size is a critical methodological decision. If your sample is too small, your results may lack precision and you run a high risk of drawing inaccurate conclusions. If your sample is unnecessarily large, you waste time, money, and resources. An appropriate sample size balances statistical accuracy with practical feasibility.
What is the Margin of Error?
The margin of error is a measure of precision. It indicates how much the results from your sample might differ from the true population value. A smaller margin of error requires a significantly larger sample size.
What Does Confidence Level Mean?
The confidence level dictates how certain you can be that the true population value falls within your calculated margin of error if you were to repeat the sampling process multiple times. A 95% confidence level is the standard in most academic and commercial research.
When This Calculator Is Not Sufficient
This calculator provides precision-based sample size estimates primarily used for proportion estimation (e.g., surveys, prevalence studies). It assumes simple random sampling. If you are performing complex hypothesis testing (e.g., comparing groups in an experiment), you should perform a formal power analysis based on your expected effect size. Additionally, complex sampling designs like stratified or cluster sampling require specialized adjustments.