Outlier Investigation Guide AI Prompt

I found outliers in my data. Help me figure out what to do with them.

Outliers found: {{outlier_description}}
Context: {{data_context}}

1. Not all outliers are the same — classify mine:
   - Data errors: the value is wrong due to a typo, system error, or import problem (e.g. a transaction amount of $10,000,000 when the max is normally $5,000)
   - Genuine extreme values: the value is correct but unusually large or small (a real whale customer who spent 100× the average)
   - Rare events: the value represents a real but infrequent event (a bulk order, a promotional spike)
   - Different population: the row represents a different type of entity than the rest (a corporate account in a dataset of individual customers)
   Based on my specific outliers, which category do they most likely fall into?

2. How to investigate:
   - For suspected data errors: check the source system, check nearby records for context, look for patterns in the error
   - For genuine extreme values: look at other columns in the same row — do they also look extreme? Or is only one column anomalous?
   - Look at the timing: did the outlier occur at a time when something unusual happened (system migration, promotional event, data export issue)?

3. What to do with them:
   - Data errors → fix or remove them. Never include known errors in your analysis.
   - Genuine extremes that are relevant → include them, but report median alongside mean (outliers inflate the mean significantly)
   - Genuine extremes that are not relevant to your question → exclude and document the exclusion transparently
   - Different population → segment them separately rather than mixing with the main group

4. The transparency rule:
   - Whatever you decide to do with outliers: document it and disclose it
   - 'We excluded 3 transactions that appeared to be data errors; including them would change the average by X%'
   - Never silently remove outliers without noting it

5. A practical check:
   - Run your analysis both with and without the outliers
   - If the conclusion is the same either way: the outliers do not matter much, keep them
   - If the conclusion changes dramatically: the outliers are doing significant work and deserve careful investigation

Return: classification of my specific outliers, investigation steps, recommended treatment, and the disclosure language to use.