Free Statistical Power Calculator
A G*Power alternative
Plan how many completed responses you need to detect a meaningful effect. Default power: 80%.
Power depends on the statistical test you plan to run.
Use the smallest meaningful effect, not the smallest possible effect.
Higher power lowers the chance of missing a real effect.
Alpha controls the false-positive risk for significance testing.
What statistical power means
Statistical power is the probability that your test will correctly find an effect when there is one. A power analysis helps you avoid running a study that is too small to find the meaningful result you care about.
The probability that your statistical test finds an effect when that effect is real.
The smallest effect you care enough to detect, usually based on common sense, prior studies, or practical importance.
The false-positive risk you accept when you call a result statistically significant.
The number of completed responses needed so the planned test is not too small to find a meaningful effect.
- Researchers often aim for 80% power, which means an 80% chance of detecting a real effect of the size you planned for.
- Expected effect size matters because very large samples can make tiny, unimportant effects statistically significant.
- Power analysis plans for inferential tests, not just descriptive survey estimates.
How to use this power analysis
Power depends on the test you plan to run, such as two groups, one sample, correlations, or proportions.
Enter the smallest effect that would matter in your study, not the smallest effect that could ever exist.
Use 80% power for a common planning baseline, or higher when missing a real effect would be costly.
Use the estimate as a planning target, then add practical buffers for incomplete or unusable responses.
Plan for Type I and Type II errors before data collection
Statistics are always estimates, so mistakes can happen. A result can be significant in your sample even when no real effect exists, or a real effect can be missed because the study was underpowered.
Something is significant in your sample, but in reality there is no effect. This is controlled by alpha.
Your sample is not significant, but in reality there is an effect. This is what low power makes more likely.
A real meaningful effect may be missed because the sample is too small for the planned analysis.
A tiny effect can become statistically significant even when it is not practically meaningful.
Power is the chance your test detects it. With 80% power, the miss risk is about 20%.
Alpha is the chance your test still calls the result significant by mistake.
Define this before launch so statistical significance does not replace practical importance.
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Launch tool →Reduce duplicate and automated survey traffic.
Launch tool →Collect real responses for your powered study.
Launch tool →Collect enough responses for the analysis you plan to run
Turn your power target into a response collection plan. Use the free exchange, buy respondents, or let SurveySwap Agency handle collection for you.
Frequently Asked Questions
Everything you need to know about planning statistical power for survey research.
What is statistical power?
Statistical power is the probability that your test will correctly find an effect when there is one.
Why do researchers use 80% power?
80% power is a common baseline. It means that if the planned effect is real, the study has about an 80% chance of detecting it.
Why does effect size matter?
With enough responses, even tiny effects can become statistically significant. Effect size keeps the plan focused on effects that are meaningful.
How is this different from a sample size calculator?
A regular survey sample size calculator plans descriptive precision. A power calculator also accounts for the statistical analysis you plan to run.
What are Type I and Type II errors?
A Type I error is a false positive. A Type II error is a false negative, where a real effect is missed because the study did not have enough power.
Can SurveySwap help collect the required sample?
Yes. You can use the free exchange, buy respondents, or work with SurveySwap Agency for done-for-you data collection.
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Turn your sample size target into completed survey responses. Use the free exchange, buy respondents, or let SurveySwap Agency handle collection for you.