Psychology Statistical Test Advisor
Need help choosing the right statistical test for your research? This smart decision tree helps you find the most appropriate analysis based on your study design, data type, and goals — no stats textbook required.
🆓 Free Version
A fast and intuitive tool for students and beginners needing simple test suggestions for basic study designs.
💼 PRO Version
Built for researchers, analysts, and instructors, the Pro version supports deeper guidance, assumption checks, and nuanced scenarios, offering contextual explanations and multiple suggestions for complex designs.
| Feature | Free Version | PRO Version |
|---|---|---|
| 🔍 Interactive test advisor | ✅ Yes | ✅ Yes |
| 🧪 Suggests test names | ✅ Yes | ✅ Yes |
| 📋 Descriptions of test purpose | ✅ Yes | ✅ Yes |
| 📊 Considers group count, data type, and pairing | ✅ Yes | ✅ Yes |
| ✅ Includes assumption checks | ❌ No | ✅ Yes |
| 🧠 Offers alternative tests if assumptions not met | ❌ No | ✅ Yes |
| 🧾 Lists test assumptions clearly | ❌ No | ✅ Yes |
| 📚 Includes context-specific advice | ❌ No | ✅ Yes |
| 🔄 Parametric vs. non-parametric logic | ❌ Basic | ✅ Advanced |
| 🧠 Supports interaction & predictive analysis | ✅ Yes (basic) | ✅ Yes (with full explanation) |
| 🔗 Suggests next steps (e.g., post-hocs, regression types) | ❌ No | ✅ Yes |
| ✍ Copy results to clipboard | ✅ Yes | ✅ Yes |
Not sure which statistical test to use for your psychology research or assignment? The Psychology Statistical Test Advisor helps you quickly find the right analysis based on your data type, number of groups, and study design. Whether you're comparing groups, testing interactions, or building predictive models, this tool makes choosing the correct statistical test fast and easy.
Simply select your study details and get an instant recommendation with clear descriptions and assumptions. Perfect for psychology students, research methods courses, dissertation writing, and social science data analysis. No downloads, no sign-ups — just straightforward guidance for statistical decisions.
Disclaimer
This tool is designed to assist psychology students and researchers with selecting a suitable test. While we strive for accuracy, the output provided is for guidance purposes only.
The creators of this tool are not responsible for decisions made based on its output. Always ensure your work aligns with the requirements of APA academic or professional standards.
Frequently Asked Questions
Psychology Statistical Test Advisor
What does this tool do?
The Statistical Test Advisor helps you determine the most appropriate statistical test to use based on:
Number of groups
Type of data (categorical, continuous)
Paired vs. independent design
What you're analyzing (e.g., group differences, associations, predictions)
It's built for psychology and social science research contexts.
How do I use this tool?
Just answer a few multiple-choice prompts:
How many groups are you comparing?
What type of data do you have?
Is the data paired (repeated measures) or independent?
What is your main research goal?
The tool will then suggest a test, such as a t-test, ANOVA, correlation, or regression, along with a short explanation.
Which tests can this tool recommend?
The tool can suggest tests such as:
Independent and paired-samples t-tests
One-way and factorial ANOVA
Repeated-measures ANOVA
Pearson and Spearman correlations
Chi-square tests
Linear regression
Mann–Whitney U, Wilcoxon, Kruskal–Wallis, etc.
Each suggestion is paired with a short description and assumptions.
Is this tool appropriate for psychology assignments and dissertations?
Yes. It’s designed for undergraduate and master’s students in psychology, education, and social sciences who are:
Writing research reports
Conducting dissertations or theses
Choosing a test before running analyses in SPSS, JASP, R, or Excel
Can this tool run the test for me?
No — it does not perform statistical calculations. It only recommends the best test based on your design and data type.
Once you’ve chosen a test, you can run it in your stats software (SPSS, JASP, Jamovi, R, etc.).
Does the tool consider test assumptions?
Yes. Each test recommendation includes a brief note on key assumptions (e.g., normality, equal variances, independence). It’s still your responsibility to check those assumptions in your dataset.
Does it recommend non-parametric alternatives?
Yes — when appropriate, the tool may suggest tests like:
Mann–Whitney U (instead of independent t-test)
Wilcoxon signed-rank (instead of paired t-test)
Kruskal–Wallis (instead of ANOVA)
Is this a substitute for a supervisor or tutor?
No — it’s a helpful starting point. While the advisor is based on accepted statistical guidelines, always confirm your test choice with a tutor, supervisor, or textbook — especially for complex designs.