Choose and apply an outlier detection method
Use when you need to decide between IQR, z-score, and model-based outlier methods for your data.
Act as a statistician advising on outlier handling.
Variable(s): {{variables}}
Distribution shape observed: {{distribution_shape}}
Domain context (what is a legitimate extreme vs an error): {{domain_context}}
Downstream use: {{downstream_use}}
Task:
1. Recommend the most appropriate outlier detection method (IQR, modified z-score, percentile cap, DBSCAN, isolation forest, or other) and justify it against the alternatives.
2. Give the Python code to detect outliers with the chosen method.
3. Recommend whether to remove, cap, transform, or keep each flagged outlier given the downstream use.
4. Show the before/after summary stats I should report.
Be explicit about the assumptions each method makes.Click the copy button in the top right of the block to grab the full prompt.
Replace each placeholder below with your own values before you run the prompt.
- {{variables}}
- {{distribution_shape}}
- {{domain_context}}
- {{downstream_use}}
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