Outlier Detection

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About This MicroSim

Explore how the 1.5 × IQR rule identifies potential outliers. The fences define boundaries, and any points beyond them are flagged as outliers.

How to Use

• Drag points to change their values and see outlier status update in real-time
• Adjust the Multiplier slider (default 1.5) to see how fence positions change
• Toggle Show Calculations to see the fence formulas
• Toggle Modified Boxplot to compare standard vs. modified whisker placement

Key Insights

• Outliers are values below Q1 - 1.5×IQR or above Q3 + 1.5×IQR
• The 1.5 multiplier is a convention, not a law
• Modified boxplots show outliers as individual points

Lesson Plan

Learning Objective

Students will apply the 1.5 × IQR rule to identify potential outliers in a dataset (Bloom's Taxonomy: Applying, Analyzing).