Outlier Calculator
Identify statistical outliers in your dataset using the IQR (Interquartile Range) method. Find unusual values that fall outside the normal range of your data.
⚠️ You need at least 4 data points to calculate quartiles. Currently: points.
📊 Statistical Summary
🎯 Detected Outliers
✅ No outliers detected in your dataset. All values fall within the normal range.
📈 Sorted Data (Outliers Highlighted)
💡 How to Use This Tool
Detect outliers in your dataset with just a few steps:
Enter Your Data
Input numbers separated by commas, spaces, or new lines.
View Statistics
See quartiles, IQR, and bounds calculated in real-time.
Identify Outliers
Outliers are highlighted and listed separately.
Copy Results
Copy the complete analysis to share or save.
📖 About Outlier Calculator
What is an Outlier?
An outlier is a data point that differs significantly from other observations in a dataset. Outliers can indicate measurement errors, data entry mistakes, or genuinely unusual values worth investigating.
The IQR Method
This calculator uses the Interquartile Range (IQR) method, a robust statistical technique for detecting outliers:
How It Works
- Sort the data in ascending order
- Find Q1 (25th percentile) - the median of the lower half
- Find Q3 (75th percentile) - the median of the upper half
- Calculate IQR = Q3 - Q1
- Lower Bound = Q1 - (1.5 × IQR)
- Upper Bound = Q3 + (1.5 × IQR)
- Values below the lower bound or above the upper bound are outliers
Common Use Cases
- Data Cleaning: Identify erroneous entries before analysis
- Quality Control: Detect defective products in manufacturing
- Finance: Find unusual transactions or market anomalies
- Research: Validate experimental data
- Sports Analytics: Identify exceptional performances
Privacy & Security
All calculations happen locally in your browser using JavaScript. Your data never leaves your device - complete privacy guaranteed.