Outlier Detector
Flag outliers with the IQR rule, z-scores or modified z-scores without deleting data.
IQR: 1.5 (mild) or 3 (extreme) · z: 3 · modified z: 3.5
Results
Continue your work
- Normality Checker — See how outliers affect the distribution.
- CSV Descriptive Statistics — Summarize every column of the dataset.
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Clear instructions
Find steps, examples and limitations below.
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Free to use
No sign-up required. Tool-specific limits may apply.
How to use the Outlier Detector
Flag outliers with the IQR rule, z-scores or modified z-scores without deleting data.
- 1 Upload or paste your data and pick the column to check.
- 2 Choose the IQR rule, z-scores or modified z-scores and a threshold.
- 3 Review the flagged rows and download them to check against the source.
Example and practical tips
Glucose values between 86 and 141 plus one value of 410 give Tukey fences of 52 to 164, so the 410 is flagged as a high outlier.
Frequently asked questions
Which method should I use?
The IQR rule and modified z-score are robust to the outliers themselves. Plain z-scores suit roughly normal data and larger samples.
Should I delete outliers?
Not automatically. First check for data-entry errors, then decide based on your analysis plan and report what you did.
Report an issue
Something broken or not quite right? Tell us and we will look into it.