Identify outliers in your dataset using multiple detection methods: IQR (Tukey's fences), Z-Score, and Modified Z-Score. Compare methods, visualize results with box plots, and analyze each data point.
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Identify outliers in any dataset using multiple statistical methods. Compare IQR (Tukey's fences), Z-Score, and Modified Z-Score detection. View results with box plot visualization and detailed data point analysis.
Outliers are data points that differ significantly from other observations. They may indicate measurement errors, data entry mistakes, or genuinely unusual values. Detecting outliers is crucial for data quality, statistical analysis, and machine learning. Common detection methods include the IQR method (using Tukey's fences), Z-Score method (measuring standard deviations from mean), and Modified Z-Score (using median and MAD for robustness).
IQR Method Formula
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The IQR (Interquartile Range) method, also known as Tukey's fences, identifies outliers as values below Q1 - 1.5×IQR or above Q3 + 1.5×IQR (mild outliers). Extreme outliers fall outside Q1 - 3×IQR and Q3 + 3×IQR. This method is robust and works well with skewed data.
Z-Score measures how many standard deviations a value is from the mean. Values with |Z| > 2 or |Z| > 3 (depending on threshold) are considered outliers. The formula is Z = (x - μ) / σ. This method assumes normally distributed data.
Use Modified Z-Score when your data is not normally distributed or contains many outliers. It uses the median and MAD (Median Absolute Deviation) instead of mean and standard deviation, making it more robust. Values with |M| > 3.5 are typically considered outliers.
IQR method is best for general use and skewed data. Z-Score works well for normally distributed data. Modified Z-Score is most robust when you suspect many outliers or non-normal distributions. Comparing all three methods gives the most complete picture.