📊 Data
Outlier Detector
Paste numbers and detect outliers using IQR or Z-score. Each value is highlighted as normal or anomalous.
Q1
12.00
Q3
14.50
IQR
2.50
Lower fence
8.25
Upper fence
18.25
Outliers found
3
All Values — Value
12
14
11
13
200⚠
15
12
14
-50⚠
13
11
15
14
12
180⚠
⚠ 3 outliers detected (shown in red)
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IQR method explained
The IQR (Interquartile Range) method defines outliers as values below Q1 − 1.5×IQR or above Q3 + 1.5×IQR. This is the method used by box plots. It's robust to extreme values and works well for skewed distributions.
Z-score method explained
A Z-score measures how many standard deviations a value is from the mean. Values with |Z| > 3 are typically considered outliers. This method assumes a roughly normal distribution.
Which method should I use?
Use IQR for skewed data or when you don't know the distribution. Use Z-score for roughly normal data. When in doubt, run both and compare.