# Check my data for bias before I draw conclusions from it

Checks who is missing from your data, how it was measured, and how it is shown, so your conclusion doesn't mislead.

- Topic: Data & Analysis
- Framework: Few-Shot
- Level: Advanced
- Tags: Fill-in Template
- Source: https://www.thinkwithprompt.com/prompts/check-my-data-for-bias-before-i-draw-conclusions-from-it

## Prompt

```text
Check my data for bias and for ways it could lead me or my audience to a misleading conclusion.

**Data and how it was collected:** [PASTE or describe: source, who is included, period, sampling method, response rate]
**Conclusion I'm likely to draw or have been asked to show:** [e.g. "customers love the new feature"]
**How it will be presented:** [e.g. "bar chart in a monthly deck", "survey summary email"]
**Audience:** [WHO will see it, and what they will decide]

Check for:
- **Selection and sampling bias**: who is in the data and who is missing (e.g. only active customers, only survey responders, only closed deals, only one region or channel).
- **Survivorship bias**: failures, churned customers, rejected applicants, or cancelled projects dropped from the data.
- **Response and measurement bias**: leading survey questions, self-reported or manager-entered numbers, incentives to over- or under-report, and changes in how things were measured.
- **Proxy bias**: a metric standing in for something it doesn't really capture (e.g. logins as "engagement", tenure as "performance").
- **Small bases**: big percentages from tiny groups, or groups too small to compare.
- **Presentation traps**: truncated axes, cumulative charts that only go up, percent vs. percentage points, missing denominators, averages shown without the spread.
- **Fairness**: groups of people who could be disadvantaged by decisions based on this data, especially if they are under-represented in it.

Return:
1. A table: Risk | Evidence in my data | How it could mislead | Severity (high / medium / low) | How to check or correct.
2. The single biggest risk to my conclusion.
3. A safer way to phrase the conclusion, and a fairer way to present the chart or number.
4. Any extra data or breakdown that would settle the question.

Separate what you can see in the data from general risks I should rule out, and don't invent figures. If decisions will affect people (hiring, pay, pricing, lending, performance ratings), recommend a review by HR, legal, or the relevant specialist before acting.
```

## What to fill in

- e.g. "customers love the new feature"
- e.g. "bar chart in a monthly deck", "survey summary email"
- WHO will see it, and what they will decide

Free to copy and use. From ThinkWithPrompt (https://www.thinkwithprompt.com/), a library of 2,663 curated AI prompts.