Effect Size + Power Visualizer for T-tests
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Cite this tool
Pass, J. C. (2025). Psychology tools: Effect Size + Power Visualizer for T-tests for Psychology Research. Simply Put Psych. https://simplyputpsych.co.uk/psych-tools
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Disclaimer
This tool is designed to assist psychology students and researchers. While we strive for accuracy, the output provided are for guidance purposes only.
The creators of this tool are not responsible for decisions made based on its output. Always ensure your analysis aligns with the requirements of your research and academic or professional standards.
Effect Size + Power Visualizer for T-tests for Psychology Research
What is this tool?
The Effect Size + Power Visualizer for T-tests is a free, web-based tool designed to help psychology students, researchers, and educators calculate statistical power and determine required sample sizes for common t-tests. This tool makes it easy to understand and visualize how effect size, statistical power, alpha level, and sample size are interconnected — essential for designing reliable and publishable research studies.
With interactive graphs and APA-style ready outputs, this tool allows you to quickly explore how changes in your study design impact statistical power — without needing to install any software or create an account.
Please note: This tool does not save or store any data
New Version of this tool available here
Who is this tool for?
Psychology students learning about statistical power, effect sizes, and experimental design.
Researchers planning studies that require formal power analyses.
Educators teaching research methods, experimental design, and statistics.
Graduate students writing theses or dissertations that require properly powered studies.
Anyone conducting t-tests and wanting to ensure appropriate sample sizes for meaningful results.
What types of analyses does it support?
This tool focuses specifically on t-tests, including:
Independent-samples t-test (between-subjects):
Comparing the means of two independent groups (e.g., experimental vs. control groups).Paired-samples t-test (within-subjects):
Comparing means from the same participants under different conditions (e.g., pre-test vs. post-test).One-sample t-test:
Comparing a sample mean to a known or hypothesized population mean.
Key Features
📈 Interactive Power vs. Sample Size graph to visualize how sample size affects power.
✍️ Automatic APA-style reporting ready to be inserted into research papers and proposals.
🎯 Support for customizable effect sizes (Cohen’s d or dz), alpha levels, and desired power.
✅ Fully browser-based, no installation, and completely free to use.
🔒 No data collection — works offline and respects user privacy.
Why use this tool?
Proper power analysis is essential to avoid underpowered studies that fail to detect meaningful effects, as well as overpowered studies that waste resources. This tool allows users to:
Plan appropriate sample sizes to achieve reliable results.
Visualize statistical power to better understand research design.
Learn about the relationship between effect size, alpha, and power — essential for students learning statistics and research methods.
⚠️ Limitations of this tool ⚠️
While this calculator is a powerful resource for basic t-tests, there are important limitations to keep in mind:
Limited to t-tests:
This tool currently only supports independent-samples, paired-samples, and one-sample t-tests. It does not support ANOVA, regression, chi-square, or correlation power analyses (future versions may add these).Assumes normally distributed data:
Calculations are based on parametric assumptions (normal distribution of data, equal variances for independent samples).Equal group sizes assumed for independent-samples t-test:
Does not currently adjust for unequal group sizes or variance heterogeneity.No adjustment for covariates or more complex models:
This tool does not handle ANCOVA, multilevel models, mediation, or moderation analysis.Educational tool for planning purposes:
While helpful for planning, this tool is not a replacement for detailed analysis software (e.g., G*Power, R, SPSS) for complex designs.
Simply Put
If you are a psychology student learning about effect sizes and statistical power, a researcher planning a new study, or an educator looking for a teaching resource, this Effect Size + Power Visualizer for T-tests is the perfect starting point.
For more advanced designs or complex power calculations, researchers should still consider tools like G*Power, R packages (pwr, simr), or consult with a statistician.