Outlier Detection Tool
Flag extreme values with IQR fences and Z-scores. Investigate before you delete.
Outlier Detector
Identify outliers in your dataset using IQR method, Z-score analysis, or both approaches
Detection Settings
Dataset Summary
Quartiles (IQR)
Outlier Detection
Data Visualization
Detected Outliers
| Index | Value | Type | Distance from Fence |
|---|---|---|---|
| 7 | 100 | extreme | 71.75 |
Detection Methods Explained
IQR Method
Uses quartiles to identify outliers. Values beyond Q1 - 1.5×IQR or Q3 + 1.5×IQR are considered mild outliers. Beyond Q1 - 3×IQR or Q3 + 3×IQR are extreme outliers.
Best for: Non-normal distributions, skewed data, robust to extreme values.
Z-Score Method
Measures how many standard deviations a value is from the mean. Values with |Z| > 2 or 3 are typically considered outliers.
Best for: Normal distributions, symmetric data, understanding relative position.
How to run a quick check
- Paste numbers (one per line or comma-separated)
- Pick IQR, Z-score, or both
- Tune the threshold if your field expects stricter or looser fences
- Read the flags—then decide keep, fix, or drop
IQR vs Z-score
IQR: Sort the data, find Q1 and Q3, set fences at Q1 − 1.5×IQR and Q3 + 1.5×IQR. Points outside those fences are candidates. No normality assumption, and extremes already in the sample do not wreck the fences the way they wreck a mean.
Z-score: Z = (x − mean) / SD. Common cutoffs are |Z| > 2, 2.5, or 3. Works best when the bulk of the data is roughly normal and n is not tiny. One huge value inflates the SD, so other extremes can hide—that is why IQR or a modified Z-score (median/MAD) often wins on messy data.
Running both is cheap. Agreement raises confidence; disagreement is a prompt to look at the distribution, not a reason to auto-delete.
What to do with flagged points
An outlier is a question, not a verdict. Typo? Sensor glitch? Real rare event? In manufacturing or lab work you often correct or discard errors. In fraud or clinical review, the extreme case is the thing you wanted. If more than about 5–10% of points flag, check the collection process or whether you mixed subgroups.
- Prefer IQR (or modified Z) when the shape is unknown or skewed
- Prefer classic Z when data look roughly normal and n is decent
- Report results with and without removals when the call is unclear
- On small samples (n < 30), be stingy about dropping anything
Frequently Asked Questions
Should I always remove outliers?
No. Find the cause first. Keep genuine extremes; fix or remove clear errors.
Which method is better?
IQR for unknown or skewed shapes; Z-score for roughly normal data. Both together is usually the most honest check.
Can outliers help the analysis?
Yes—breakthroughs, failures, and fraud often live in the tails.
What about skewed or multimodal data?
Trust IQR more than raw Z-scores. Split groups or transform if the shape is wild.
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