Sample Size Calculator
Determine the minimum sample size required for a survey to ensure statistical significance. Calculate respondent numbers based on confidence level, margin of error, and population.
Leave blank if the population is very large (e.g. > 20,000).
Use 50% if unknown (this gives the most conservative sample size).
How to Calculate Sample Size
When conducting a survey, you rarely have the resources to ask the entire population. Instead, you survey a small subset (a sample) to infer data about the whole. The Sample Size tells you how many people you need to interview to trust your results.
Key Terms
- Confidence Level: How sure you want to be that your data is accurate. A 95% confidence level means if you repeated the survey 100 times, 95 of them would yield the same result.
- Margin of Error: The amount of random sampling error in your survey results. If you find that 60% of people like pizza with a 5% margin of error, the true number is likely between 55% and 65%.
- Population Size: The total number of people in the group you are studying. If the population is large (e.g., all adults in the US), knowing the exact number doesn't change the sample size much.
Standard Formula
For infinite populations, the formula is:n = (Z² × p × (1-p)) / e²
- Z: Z-score (1.96 for 95% confidence).
- p: Population proportion (assumed 0.5 or 50% for maximum variability).
- e: Margin of error (decimal).
? Frequently Asked Questions
A sample size of 385 is often the 'magic number' for many surveys because it provides a 5% margin of error at a 95% confidence level for any large population.
If you don't know what percentage of people will choose an answer, assuming 50% is the safest bet. It yields the largest possible sample size, guaranteeing your accuracy regardless of the actual result.
Only if the population is small. For populations larger than 20,000, increasing the population size has almost no effect on the required sample size.