IQR Outlier Detector Identify Anomalies with Interquartile Range Method
Detect outliers using the robust Interquartile Range (IQR) method. Supports both Tukey fences and MAD detection with 100% client-side processing for complete data privacy.
π Quick Start
Enter Your Data
Choose Detection Method
View Detected Outliers
Data Input
Supported Data Formats:
- Comma-separated: 1.5, 2.8, 9.1, 16.2
- Space-separated: 1.5 2.8 9.1 16.2
- Newline-separated: one number per line
- Scientific notation: 1.23e-4, 5.67E+8
- Series mode: use "Group Name: value1, value2, value3" per line to compare multiple groups
- Automatically ignores text and special characters
Outlier Detection Results
Outlier Values:
Interpretation: Enter data to see interpretation
Data Distribution & Outliers
Enter data to generate box plot
Please enter at least 4 numbers above to start calculating
Box Plot Visualization
This box plot visualizes your data distribution. The box shows the interquartile range (IQR) containing the middle 50% of data. The line inside the box represents the median. Whiskers extend to show the range, and red dots indicate outliers.
Outlier Method: Tukey (1.5ΓIQR)
Box (IQR) / Legend
- Box (IQR)
- Median Line
- Whisker
- Outliers
Combined Summary
Export Results
Detection Method
Outlier detection identifies data points that fall significantly outside the expected range. We support two robust methods:
Tukey Method (1.5ΓIQR)
Uses the Interquartile Range (IQR) to define fences. Any point beyond Q1 - 1.5ΓIQR or Q3 + 1.5ΓIQR is flagged as an outlier. This is the standard method used in box plots.
MAD Method
Uses Median Absolute Deviation for robust outlier detection. Better suited for skewed distributions or when the data contains many outliers.
Learn More
Complete Guide to IQR Method Outlier Detection
Learn how the IQR method works, understand Tukey fences, and see real-world examples of outlier detection.
MAD vs Tukey: Choosing the Right Outlier Detection Method
Compare MAD and Tukey methods to understand when to use each approach for different data distributions.
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Frequently Asked Questions
What is the IQR method for outlier detection?
The IQR (Interquartile Range) method uses quartiles to identify outliers. Any data point beyond 1.5 times the IQR from Q1 or Q3 is considered an outlier. This is the standard method used in box plots.
When should I use Tukey vs MAD method?
Use Tukey method for general-purpose outlier detection with normally distributed or moderately skewed data. Use MAD method when your data is heavily skewed or contains many outliers, as MAD is more robust to extreme values.
What does it mean if no outliers are detected?
If no outliers are detected, all your data points fall within the expected range (within the fences). This suggests your data is relatively consistent, though it doesn't guarantee there are no unusual valuesβjust that they don't exceed the statistical thresholds.
Should I remove outliers from my dataset?
Not necessarily. Outliers may represent valid extreme values (e.g., top performers, rare events) or data errors. Always investigate outliers before removing them. Consider the context and whether they provide meaningful insights or indicate data quality issues.
Is my data safe with this detector?
Yes! All calculations happen in your browser. Your data never leaves your device and is not sent to any servers. The tool works completely offline.
Recent Updates
Added MAD outlier detection
Now supports both Tukey and MAD methods for robust outlier detection
Enhanced visualization
Outliers are now clearly highlighted in the box plot visualization