Sample Size Calculator
Determine the optimal sample size for your research with this comprehensive power analysis calculator. Features support for multiple statistical tests including t-tests, ANOVA, correlation, regression, and proportions. Includes interactive power curves, effect size guidelines based on Cohen's conventions, and detailed calculation results with formulas and references. Essential for research planning, grant applications, and ensuring your study has adequate statistical power to detect meaningful effects.
Key Features
- Power analysis for 9+ statistical test types
- Interactive power curve visualization
- Effect size presets (small, medium, large)
- Custom effect size input options
- Support for one-tailed and two-tailed tests
- Calculation history tracking
- Detailed results with formulas
- Test-specific parameter inputs
- Guidelines for effect sizes and power
- Sample size per group calculations
- References to statistical literature
- Tips for research planning
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Frequently Asked Questions
What is power analysis and why do I need it?
Power analysis determines the minimum sample size needed to detect an effect of a given size with a specified level of confidence. It prevents two problems: (1) studies too small to detect real effects (Type II error/false negatives), and (2) studies unnecessarily large, wasting resources. Power analysis is typically required in grant proposals and should be conducted before data collection begins.
What power level should I use for my study?
The conventional standard is 0.80 (80% power), meaning your study has an 80% chance of detecting a true effect if it exists. Some fields require higher power (0.90 or 90%) for confirmatory studies. Power below 0.80 is generally considered underpowered and risks missing real effects. This tool helps you calculate sample sizes for different power levels.
How do I determine effect size for my study?
Effect size can be determined from: (1) prior research on similar topics, (2) pilot study data, (3) Cohen's conventions (small: d=0.2, medium: d=0.5, large: d=0.8), or (4) minimum practically meaningful difference. For dissertation planning, review similar published studies in your field to estimate realistic effect sizes. This tool provides effect size presets and guidelines.
What is the difference between alpha and beta in power analysis?
Alpha (α) is the significance level (typically 0.05), representing the probability of Type I error (false positive - finding an effect that does not exist). Beta (β) is the probability of Type II error (false negative - missing an effect that does exist). Power = 1 - β, so with 80% power, β = 0.20 (20% chance of missing a real effect).
Is this power analysis calculator free and accurate?
Yes, completely free with no registration required. Our calculations use established statistical formulas from Cohen (1988) and other standard references. Results match those from commercial statistics packages like G*Power. However, always consult with a statistician for complex designs or when making final sample size decisions for major studies.