Back to guide/Research & Analysis

Statistical Analysis Guide

Listing confounders and asking for assumption checks prevents the common mistake of running a test on data that violates its assumptions. The plain-English interpretation ensures you understand the results.

chatgpt-prompts-for-data-analysishypothesisdataset_descriptiondependent_var
Edit View
Prompt
I need to analyze whether {{hypothesis}} using the following data:

Dataset: {{dataset_description}}
Variables:
- Dependent variable: {{dependent_var}} ({{dv_type}})
- Independent variable(s): {{independent_vars}}
- Potential confounders: {{confounders}}
Sample size: {{sample_size}}
Significance level: {{alpha}}

Walk me through:
1. Which statistical test is appropriate and why (consider alternatives)
2. Assumptions to check before running the test, with code to check each one
3. The {{language}} code to run the analysis
4. How to interpret the output — what numbers matter and what they mean in plain English
5. How to report the results in a paper or presentation (APA format if applicable)
6. Limitations of this analysis and what could strengthen the conclusion

Variables to customize

{{hypothesis}}{{dataset_description}}{{dependent_var}}{{dv_type}}{{independent_vars}}{{confounders}}{{sample_size}}{{alpha}}{{language}}

Why this prompt works

Listing confounders and asking for assumption checks prevents the common mistake of running a test on data that violates its assumptions. The plain-English interpretation ensures you understand the results.

Save this prompt to your library

Organize, version, and access your best prompts across ChatGPT, Claude, and Cursor.

Related prompts

SQL Query Writer

Providing the full schema and relationships eliminates guesswork. Asking for comments and assumption flags makes the output reviewable and trustworthy before running against production.

Data Cleaning Script

Requiring before/after logging and a summary report turns a cleaning script into an auditable process. Flagging rows for manual review prevents silent data loss.

Visualization Recommendation

Starting from the insight rather than the chart type ensures the visualization serves the story. Asking for common mistakes prevents the most frequent data viz errors.

Dashboard Design Spec

Defining the key questions the dashboard must answer at a glance prevents scope creep and ensures every widget earns its space. Specifying the audience expertise level calibrates complexity.