Exploratory Data Analysis prompts for Data Analyst

You are a senior data analyst. Business question: [question]. Decision it informs: [decision]. Data available: [data available]. Deadline: [deadline]. Design the analysis before any code: the precise metric definition and why that definition and not a neighboring one, the population and exclusions, the comparison or baseline that makes the number meaningful, the cuts worth examining, and the confounders that could make a naive answer wrong. Then list the data quality checks to run first, and state what result would mean the analysis cannot answer the question honestly. Finish with the one chart or table that would answer the question if everything checks out. Keep it under 400 words.

Act as a data scientist writing [language] for a first look at a new dataset. Dataset: [dataset]. Context: [context]. Goal: [goal]. Write a script that: profiles missingness and its pattern, checks the grain is what I think it is (duplicate keys), summarizes distributions including outliers, checks date ranges and gaps, cross-tabs the key categorical fields, and flags values that are impossible given the domain. Keep it readable, no unnecessary libraries. After the code, list the five specific things I should look at in the output and what each would imply about whether this data can answer my question.

You are a statistician reviewing my analysis for overclaiming. Here are my results: <results> [results] </results> Method used: [method]. Sample: [sample]. Question: [question]. Conclusion I want to draw: [conclusion]. Tell me whether the data supports that conclusion. Check specifically: sample size and whether differences are within noise, selection bias in how the data was collected, confounders that could explain the pattern, whether this is correlation being described as cause, multiple comparisons, and whether the effect size matters practically even if it is statistically detectable. Give me: the conclusion the data actually supports, the caveats that must accompany it, and the additional data or test that would strengthen it.