Spot errors or outliers in a small dataset
Works with ChatGPT · Claude · Copilot
Catch likely mistakes or odd values before you rely on data.
The problem
Bad rows and outliers quietly distort conclusions.
What this skill does
AI flags values that look wrong, impossible, or inconsistent.
When to use it
- Use before analyzing or sharing a dataset.
Juni’s tip
Juni says: one wrong cell can flip a conclusion — scan before you trust.
The prompt
Try it live
Fill in the blanks — your ready-to-use prompt builds as you type.
Weak vs. better
is my data okay
Scan this dataset — [paste data] — for data-quality issues: impossible values, likely typos, inconsistent formats, duplicates, and outliers. For each, say why it's suspicious and suggest how to check it. Don't auto-delete anything.
Why this works
Catching suspect values before analysis keeps a few bad rows from skewing the whole conclusion.
Practice task
Run a small dataset through an error-and-outlier check.
Self-check
- Flags impossible values and likely typos
- Identifies duplicates and format issues
- Suggests how to verify, not auto-delete
Common mistakes to avoid
- Assuming the data is clean
- Deleting outliers without checking them
- Missing inconsistent formats or duplicates
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