Interpreting Statistical Results
Interpreting Results of Hypothesis Testing for Decision-Making
This prompt helps data science teams explain the results of hypothesis testing in a way that supports strategic decision-making. It focuses on breaking down the statistical findings, explaining their implications, and translating them into actionable recommendations.
Responsible:
Data Science
Accountable, Informed or Consulted:
Data Science, Product, Marketing, 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:
Define the hypothesis test setup, including the null and alternative hypotheses, test type, and significance level.
Collect key test outputs, such as the p-value, confidence intervals, and test statistic.
Identify the practical implications of the hypotheses for decision-making.
THE PROMPT
Help interpret the results of a hypothesis test conducted to evaluate [specific scenario, e.g., the effectiveness of a new marketing campaign]. Focus on:
Test Purpose and Hypotheses: Recommending a clear summary, such as, ‘Explain the purpose of the test, including the null and alternative hypotheses, and how they relate to the decision context.’
Statistical Results: Suggesting clarity, like, ‘Provide an explanation of the key findings, such as the p-value, test statistic, and confidence intervals, and their statistical significance.’
Real-World Implications: Including actionable insights, such as, ‘Discuss whether the findings support or refute the null hypothesis and what that means for actions like scaling the campaign or revising the strategy.’
Limitations: Proposing transparency, such as, ‘Highlight any potential limitations in the data, test assumptions, or sample size that could affect the reliability of the conclusions.’
Next Steps: Recommending follow-ups, such as, ‘Suggest further tests, refinements to the analysis, or additional data collection to validate the findings.’
Provide a clear interpretation of the hypothesis test results with actionable recommendations and considerations for next steps. If additional details about the test or audience are needed, ask clarifying questions to refine the explanation.
Bonus Add-On Prompts
Propose methods for visualizing hypothesis test results, such as confidence interval plots or distributions.
Suggest ways to contextualize test results within the broader goals of a project or strategy.
Highlight techniques for explaining Type I and Type II errors to non-technical audiences.
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 explaining specific hypothesis tests, like t-tests, ANOVA, or chi-square tests.
Include tips for communicating results to different audiences, such as executives or analysts.
Propose ways to connect test results to KPIs or business metrics.
Highlight tools like R or Python for generating visualizations of test outcomes.
Add suggestions for contextualizing findings with historical data or prior tests.
WHEN TO USE
To explain hypothesis testing results for strategic or tactical decisions.
During presentations or reports where statistical evidence supports key recommendations.
When aligning statistical results with broader organizational or project goals.
WHEN NOT TO USE
For purely exploratory or descriptive analyses.
If the test assumptions or results are unclear or invalid.