Interpreting Statistical Results
Simplifying Statistical Test Results for Non-Technical Audiences
This prompt helps data science teams translate complex statistical test results into clear, actionable insights for stakeholders with minimal technical background. It focuses on breaking down terminology, explaining significance, and emphasizing the real-world implications of findings.
Responsible:
Data Science
Accountable, Informed or Consulted:
Data Science, Marketing, Product, Operations
THE PREP
Creating effective prompts involves tailoring them with detailed, relevant information and uploading documents that provide the best context. Prompts act as a framework to guide the response, but specificity and customization ensure the most accurate and helpful results. Use these prep tips to get the most out of this prompt:
Gather the statistical test results, including key metrics like p-values, confidence intervals, and test statistics.
Identify the target audience’s level of statistical knowledge and the context of the decision-making process.
Define the practical significance of the test results and how they align with organizational goals.
THE PROMPT
Help interpret the results of [specific statistical test, e.g., a chi-square test for independence] in a way that is clear and actionable for a non-technical audience. Focus on:
Test Overview: Recommending an explanation, such as, ‘Provide a brief summary of the test’s purpose, for example, determining if there is a significant relationship between [variable A] and [variable B].’
Key Results: Suggesting clarity, like, ‘Explain the main findings, such as whether the p-value indicates statistical significance, and what that means in practical terms.’
Real-World Impact: Including actionable implications, such as, ‘Describe how the results affect decision-making, for example, confirming the need to target [specific segment] more aggressively.’
Avoiding Jargon: Proposing simplifications, such as, ‘Replace technical terms with relatable language, like explaining "confidence intervals" as the range where we expect results to fall most of the time.’
Next Steps: Recommending actions, such as, ‘Suggest follow-up steps based on the results, like conducting further tests or adjusting [specific strategy].’
Provide a clear interpretation of the statistical test results that highlights their significance and impact in a way that resonates with non-technical stakeholders. If additional details about the audience or context are needed, ask clarifying questions to refine the explanation
Bonus Add-On Prompts
Propose strategies for visualizing statistical results to enhance understanding for non-technical audiences.
Suggest methods for explaining statistical errors or uncertainty in simple terms.
Highlight techniques for aligning statistical results with specific business objectives.
Use AI responsibly by verifying its outputs, as it may occasionally generate inaccurate or incomplete information. Treat AI as a tool to support your decision-making, ensuring human oversight and professional judgment for critical or sensitive use cases.
SUGGESTIONS TO IMPROVE
Focus on simplifying results from specific tests, like t-tests, ANOVA, or regression models.
Include tips for comparing multiple test results and explaining differences.
Propose ways to use infographics or dashboards to visualize statistical results.
Highlight tools like Tableau or Power BI for interactive result presentations.
Add suggestions for incorporating real-world examples to make findings relatable.
WHEN TO USE
To communicate statistical findings to non-technical stakeholders or executives.
During presentations or reports that require actionable insights from data analysis.
When translating technical results into practical strategies or recommendations.
WHEN NOT TO USE
For technical audiences who require detailed statistical explanations.
If the statistical analysis results are incomplete or inconclusive.