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Introduction to Open Power Samplesize
Statistical power is the probability that a study will detect an effect when there is an effect to be detected. If statistical power is high, the probability of making a Type II error, or concluding there is no effect when, in fact, there is one, goes down.
Sample size is the number of individual samples measured or observations used in a survey or experiment. A larger sample size provides more accurate results, but can be costly and time-consuming.
Calculating power and sample size is a critical step in the design of many research studies. An underpowered study is unlikely to find a true effect, making it a waste of time and resources. An overpowered study uses more participants than necessary, which can be unethical and wasteful.
Open Power Samplesize is a free, open-source web application designed to help researchers and students perform power and sample size calculations for various statistical tests. Our goal is to provide an intuitive and accessible tool for study design.
This tool supports a wide range of scenarios, including tests for means, proportions, time-to-event data, and more.
To start, head over to the Calculator and select the statistical test that matches your research design. Each calculator provides fields for the necessary parameters and will generate results and plots to help you understand the relationship between sample size, power, and effect size.
For more detailed instructions, please visit our Usage and References sections.
For more detailed statistical background, please visit our Blog.