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Calculate the optimal sample size for your surveys and studies based on confidence level, margin of error, and population size.
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Determine the right sample size for your research project to ensure statistical significance and reliable results.
What margin of error can you accept? 5% is a common choice | % | The margin of error is the amount of error that you can tolerate. If 90% of respondents answer yes, while 10% answer no, you may be able to tolerate a larger amount of error than if the respondents are split 50-50 or 45-55. Lower margin of error requires a larger sample size. |
What confidence level do you need? Typical choices are 90%, 95%, or 99% | % | The confidence level is the amount of uncertainty you can tolerate. Suppose that you have 20 yes-no questions in your survey. With a confidence level of 95%, you would expect that for one of the questions (1 in 20), the percentage of people who answer yes would be more than the margin of error away from the true answer. Higher confidence level requires a larger sample size. |
What is the population size? If you don't know, use 20000 | How many people are there to choose your random sample from? The sample size doesn't change much for populations larger than 20,000. | |
What is the response distribution? Leave this as 50% | % | For each question, what do you expect the results will be? If the sample is skewed highly one way or the other, the population probably is, too. If you don't know, use 50%, which gives the largest sample size. |
Your recommended sample size is | 377 | This is the minimum recommended size of your survey. If you create a sample of this many people and get responses from everyone, you're more likely to get a correct answer than you would from a large sample where only a small percentage of the sample responds to your survey. |
| With a sample size of | 377 | With a confidence level of | 90% | 95% | 99% |
|---|---|---|---|---|---|
| Your margin of error would be | 5.04% | Your sample size would need to be | 267 | 377 | 643 |
If 50% of all the people in a population of 20,000 people drink coffee in the morning, and if you were to repeat the survey of 377 people ("Did you drink coffee this morning?") many times, then 95% of the time, your survey would find that between 45% and 55% of the people in your sample answered "Yes".
The remaining 5% of the time, or for 1 in 20 survey questions, you would expect the survey response to be more than the margin of error away from the true answer. When you survey a sample of the population, you don't know that you've found the correct answer, but you do know that there's a 95% chance that you're within the margin of error of the correct answer.
Try changing your sample size and watch what happens to the alternate scenarios. That tells you what happens if you don't use the recommended sample size, and how margin of error and confidence level (that 95%) are related.
About Response Distribution: If you ask a random sample of 10 people if they like donuts, and 9 of them say, "Yes", then the prediction that you make about the general population is different than it would be if 5 had said, "Yes", and 5 had said, "No". Setting the response distribution to 50% is the most conservative assumption. So just leave it at 50% unless you know what you're doing.
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