What is Outlier Detection?
Outlier detection identifies data points that deviate significantly from the rest. This tool uses both the IQR method (Tukey fences) and Z-score method simultaneously, providing a more reliable picture of which points warrant investigation.
Frequently Asked Questions
How are outliers detected?
Two methods: IQR-based (Tukey fences: values beyond Q1 − 1.5×IQR or Q3 + 1.5×IQR) and Z-score (values more than 3 standard deviations from the mean).
Which outlier method should I use?
IQR is robust to non-normal distributions and is the industry standard. Z-score is sensitive to extreme values and works best for approximately normal data.
Key Terms
- IQR Method
- Data points outside [Q1−1.5×IQR, Q3+1.5×IQR] are flagged as outliers.
- Z-Score Method
- Values with |z| > 3 are considered statistical outliers.
- Tukey Fences
- The boundary defined by Q1−1.5×IQR and Q3+1.5×IQR.
Computation Method
- Quartiles are calculated using linear interpolation (method 7 from Hyndman & Fan, 1996), consistent with Python's NumPy and pandas defaults.
- Outlier detection uses the Tukey fences method: lower fence = Q1 − 1.5 × IQR, upper fence = Q3 + 1.5 × IQR.
- Chart scaling caps the display axis at the upper fence value so the visualization remains clear even when extreme outliers are present.
References
- Tukey, J. W. (1977). Exploratory Data Analysis. Addison-Wesley.
- Iglewicz, B. & Hoaglin, D. C. (1993). How to Detect and Handle Outliers. ASQC Quality Press.
- Barnett, V. & Lewis, T. (1994). Outliers in Statistical Data. 3rd ed. Wiley.