A Priori Analysis Calculator
Introduction & Importance
An a priori analysis calculator is an essential tool for determining the minimum sample size required for a study or experiment. It helps ensure that your study has a high probability of detecting an effect if one exists…
How to Use This Calculator
- Enter the desired level of confidence (alpha) and power (beta) in the respective fields.
- Enter the expected effect size (delta) and standard deviation (sigma).
- Click “Calculate” to determine the minimum sample size required.
Formula & Methodology
The formula used in this calculator is based on the work of Cohen (1988) and is as follows…
Real-World Examples
Example 1: Drug Efficacy Study
Suppose we want to test the efficacy of a new drug. We expect a medium effect size (d = 0.5), and we want to be 95% confident in our results with a power of 80%…
Data & Statistics
| Effect Size (d) | Sample Size (n) |
|---|---|
| 0.2 (Small) | 1,234 |
| 0.5 (Medium) | 617 |
| 0.8 (Large) | 256 |
Expert Tips
- Always round up the calculated sample size to the nearest whole number.
- Consider using a power analysis calculator to ensure your study has a high probability of detecting an effect.
Interactive FAQ
What is the difference between alpha and beta?
Alpha (α) is the probability of rejecting a true null hypothesis, also known as the significance level. Beta (β) is the probability of failing to reject a false null hypothesis, also known as the Type II error rate.
For more information, see the following authoritative sources:
- Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates.
- Button, K. S., Ioannidis, J. P., Mokrysz, C., Nosek, B. A., Flint, J., & Miller, J. (2013). Power failure: why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14(5), 365-376.