How to Use the Statistical Power Calculator

A Statistical Power Calculator helps researchers estimate the probability that their study will detect a true effect if one exists, given a sample size, effect size, and significance level.

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What Is Statistical Power?

Statistical power is the likelihood of rejecting the null hypothesis when it is false. In simple terms, it answers the question: "If there is a real effect, how likely is my study to detect it?"

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Steps to Use the Calculator

1. Enter the sample size (n) – The number of subjects or observations in your experiment or survey.
2. Enter the effect size (Cohen’s d) – A standardized way to represent the magnitude of an effect (e.g., 0.2 = small, 0.5 = medium, 0.8 = large).
3. Enter the significance level (alpha) – The probability of a Type I error (usually set at 0.05).
4. Click “Calculate Power” – The calculator will return the estimated power of your test.

A power value of 0.8 or higher is typically considered adequate for most scientific studies.

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FAQ: Statistical Power Calculator

Q1: What is a good value for statistical power?
A: A power of 0.80 (80%) or higher is commonly accepted, meaning there's an 80% chance of detecting a true effect.

Q2: What is Cohen’s d?
A: Cohen’s d is a standardized measure of effect size used for comparing two means.

• 0.2 = small
• 0.5 = medium
• 0.8 = large

Q3: Why do I need to input the significance level (alpha)?
A: Alpha determines your tolerance for false positives (Type I errors). Common values are 0.01, 0.05, or 0.10.

Q4: Is this calculator valid for all test types?
A: This is a simplified calculator for two-group comparisons (e.g., t-tests) using a normal approximation. For complex designs (ANOVA, regression), use statistical software like G*Power or R.

Q5: What if my power is too low?
A: You may need to increase your sample size or reconsider your expected effect size to improve the power.