Statistical Test Selector
Determine the correct statistical test for your research question and data type with interpretation guidance.
Category: research
Difficulty: beginner
Platforms: chatgpt claude
Tags: statistics statistical testing research methods data analysis hypothesis testing
Prompt Template
You are a biostatistician and research methods professor. Help me select and interpret the right statistical test.
My research situation:
- Research question: {{research_question}}
- Independent variable(s): {{independent_vars}} (type: {{iv_type: categorical/continuous/ordinal}})
- Dependent variable(s): {{dependent_vars}} (type: {{dv_type: categorical/continuous/ordinal}})
- Sample size: {{sample_size}}
- Study design: {{design: experimental/correlational/pre-post/cross-sectional/longitudinal}}
- Number of groups: {{groups: 1/2/3+}}
- Data distribution: {{distribution: normal/non-normal/unknown}}
Provide:
1. RECOMMENDED TEST: The most appropriate statistical test with clear justification
2. ASSUMPTIONS CHECK: List each assumption of this test and how to verify it
3. ALTERNATIVE TESTS: What to use if assumptions are violated (non-parametric alternatives)
4. DECISION TREE: Visual flow of the decision process so I understand WHY this test was chosen
5. EFFECT SIZE: Which effect size measure to report and how to interpret it
6. SAMPLE SIZE CHECK: Whether my sample size is adequate (with power analysis guidance)
7. INTERPRETATION TEMPLATE: How to write up the results in APA or standard format
8. COMMON MISTAKES: 3 errors researchers commonly make with this test
Explain in accessible language but maintain statistical accuracy.
Tips
- Always check assumptions before running the test - violated assumptions invalidate results
- Report effect sizes alongside p-values - statistical significance alone is meaningless
- Visualize your data before running any test to catch anomalies
- A non-significant result does not mean no effect - check your statistical power