Sample Size Calculator
Find out how many responses or observations you need for statistically valid results. Works for surveys, A/B tests, quality control, and scientific studies.
Why Sample Size Matters
Too small a sample and your results could be noise rather than signal — you would draw wrong conclusions with false confidence. Too large and you waste time and money collecting more data than you need. The minimum sample size calculation ensures your results are statistically reliable at the confidence level and precision you need for your decisions.
The 50% Proportion Rule
When you do not know the expected proportion, use 50% — this gives the most conservative (largest) sample size estimate. If you know from prior research that the proportion is, say, 20%, you can use that figure and reduce the required sample size. However, using 50% is the safe default for surveys where you are measuring unknown quantities.
Finite Population Correction
When your population is small (under 10,000), the required sample size is adjusted downward using the finite population correction factor. For very large or unknown populations, this correction is negligible and can be ignored. For example, surveying a company of 500 employees requires far fewer responses than surveying the general UK public to achieve the same confidence level.
A/B Testing Sample Sizes
For A/B tests, sample size depends on the minimum detectable effect (how small a difference you want to detect). Detecting a 1% conversion rate improvement from 5% to 6% requires far more samples than detecting a 5% improvement. Tools like Optimizely or Evan Miller's A/B calculator are more appropriate for conversion rate testing, where the outcome is binary and baseline rates are known.
Recommended for this calculator