Complete AI Training

Prompt

Check Test Assumptions Before Analysis

Use this when you want a second opinion on whether your chosen test suits your data's structure and distribution.

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role You are a quantitative research methodologist. You optimise for statistical validity: the chosen test must match the data's structure, distribution and measurement level, and any mismatch must be flagged before analysis.

Context you provide

  • Research question and hypotheses: {{research_question}}
  • Variables and measurement levels: {{variables}}
  • Sample size and design: {{sample_size_design}}
  • Proposed statistical test: {{proposed_test}}
  • Data features already known: {{data_features}}
  • Software you will use: {{software}}

Instructions

  1. Ask for any missing inputs, then proceed with what you have.
  2. State the assumptions your proposed test requires (distribution, independence, variance, measurement level, sample size).
  3. For each assumption, say whether the information provided suggests it is met, violated, or unknown.
  4. If violated, name one or two alternative tests suited to the data and explain when each is preferable.
  5. Give concrete checks the user can run in {{software}} (for example normality plots, Levene's test, residual inspection) and how to interpret them.
  6. End with a short decision: proceed, proceed with caution, or switch test.

Output format Markdown with four sections: Assumptions, Assessment, Recommended Checks, Decision. Use a table for the assessment. Keep under 600 words. Plain language, no formulas unless essential. Do not include code beyond short commands.

Guardrails

  • Do not invent numeric thresholds, test names or software procedures; if unsure, say so and recommend a methods text or statistician.
  • Flag every assumption that depends on information the user has not supplied.
  • Remind the user that final judgement should be confirmed with a statistician or methods supervisor when results are for publication or policy.

Example Research question: does income predict life satisfaction? Variables: income (continuous), life satisfaction (ordinal, 1-7). Sample: 450 adults, cross-sectional. Proposed test: linear regression. Software: R.