How to Use the Z-Score & IQR Statistical Outlier Detector
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Paste numbers separated by commas, spaces, or newlines.
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Select your outlier detection method: IQR (Tukey's 1.5×IQR Rule) or Z-Score (|z| > 2.5 or 3.0 threshold).
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Examine the computed summary metrics: Q1, Q3, IQR, Lower Fence, Upper Fence, Mean, and Standard Deviation.
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View highlighted anomalous values and download or copy the clean outlier-free dataset.
Practical Example & Use Case
Common Real-World Scenario
Detecting anomalous transaction amounts or server latency spikes in a sample distribution.
Technical Notes & Limitations
Browser Engine Architecture
Computes standard parametric (Z-score) and non-parametric (Tukey IQR) anomaly boundaries in browser memory.
Frequently Asked Questions
When should I use IQR versus Z-Score for outlier detection?
Use IQR when your data is skewed or has heavy tails (non-normal); use Z-Score when your data is roughly normally distributed.