Power Analysis Calculator | Sample Size & Statistical Power Tool

Analysis Parameters

Statistical Power (1-β)

0.50 0.80 0.99

Probability of detecting an effect if it exists (default: 0.8)

Significance Level (α)

0.01 0.05 0.10

Probability of Type I error (false positive)

Effect Size (Cohen's d)

Standardized difference between groups

Test Type

One-tailed Two-tailed

Directionality of hypothesis test

Allocation Ratio (Treatment:Control)

Ratio of participants in treatment vs control groups

Attrition/Dropout Rate (%)

0% 10% 30%

Expected participant dropout during study

Additional Tools

Analysis Results

Required Sample Size

Total participants needed

With Dropout

Adjusted for attrition

Treatment Group

Control Group

Total Power

0.80

Sample Size Visualization

36 Treatment

36 Control

72 With Dropout

Interpretation

With 80% power, α=0.05 (two-tailed), and a medium effect size (d=0.5), you need 64 participants for your study. Accounting for a 10% attrition rate, recruit 72 participants total (36 per group).

Recent Calculations

Power	Effect	Sample	Date

How to Use the Power Analysis Calculator for Your Research

Power analysis is a critical step in designing research studies, experiments, and clinical trials. This tool helps you determine the appropriate sample size needed to detect an effect if one truly exists. Here's a comprehensive guide to using our Power Analysis Calculator effectively.

Understanding the Parameters

Statistical Power (1-β)

This is the probability of correctly rejecting a false null hypothesis (detecting an effect when it exists). Most research uses 80% power (0.8), meaning you have an 80% chance of detecting an effect of the specified size.

Significance Level (α)

The probability of a Type I error (false positive). Commonly set at 0.05 (5%), meaning there's a 5% chance of concluding an effect exists when it doesn't. Lower α values (like 0.01) reduce false positives but require larger samples.

Effect Size (Cohen's d)

The standardized difference between groups you expect to detect. Small (0.2), medium (0.5), or large (0.8) effects. Smaller effects require larger sample sizes to detect. Choose based on prior research or practical significance.

Allocation Ratio

The ratio of participants in treatment versus control groups. A 1:1 ratio is most statistically efficient, but sometimes you may need more participants in one group (e.g., in clinical trials with multiple treatment arms).

Step-by-Step Guide

Set your desired power using the slider (typically 0.8 for 80% power).
Choose your significance level (α) - usually 0.05 for p < .05.
Select the expected effect size based on prior research or pilot studies.
Choose between one-tailed or two-tailed test based on your hypothesis directionality.
Adjust the allocation ratio if your groups won't be equal in size.
Account for attrition by setting an expected dropout rate.
Click "Calculate Sample Size" to see your results in real-time.

Advanced Features

Our calculator includes several advanced tools:

Find Required Power: Determine the power you can achieve with a fixed sample size.
Detectable Effect Size: Calculate the smallest effect you can detect with your available sample.
Save Scenario: Store different parameter combinations for comparison.
Compare Scenarios: View multiple analysis scenarios side-by-side.
Export Results: Download your calculations as a CSV or PDF report.

Best Practices for Power Analysis

1. Conduct power analysis before data collection to ensure adequate sample size.

2. Use effect sizes from similar published studies or conduct a pilot study.

3. Always account for attrition and missing data in your sample size calculation.

4. For complex designs, consider consulting a statistician in addition to using this tool.

5. Report all power analysis parameters in your methods section for transparency.

Note: This calculator uses standard formulas for two-group comparisons. For more complex designs (multivariate, repeated measures, non-parametric tests), specialized software may be required.