Outlier Detection in Data Sets

Overview

Outliers—data points that sit far away from the bulk of observations—can skew averages, inflate variances, and seed misleading insights. Detecting and handling them is therefore a foundational step in any robust data-engineering or analytics pipeline.

Why Outlier Detection Matters

Whether you are forecasting revenue, monitoring sensor health, or training a machine-learning model, anomalies can have a dramatic impact on your conclusions. In production data systems, undetected outliers may:

• Trigger false alerts, wasting engineering hours.
• Bias statistical models, leading to poor predictions.
• Mask critical safety issues in IoT or healthcare data streams.
• Distort KPIs presented in dashboards or embedded analytics.

Systematically surfacing these extreme values ensures your downstream analytics remain trustworthy and actionable.

Key Concepts and Terminology

Univariate vs. Multivariate Outliers

Univariate outliers deviate within a single variable (e.g., a salary of $5M in a dataset of hourly employees). Multivariate outliers are normal in isolation but unusual in combination (e.g., a young executive earning a C-level salary).

Parametric vs. Non-Parametric Methods

Parametric techniques (e.g., Z-score) assume an underlying distribution such as Gaussian, while non-parametric approaches (e.g., IQR, Median Absolute Deviation) make minimal assumptions.

Core Detection Techniques

IQR (Interquartile Range)

The classic box-plot rule flags a value < Q1 – 1.5 × IQR or > Q3 + 1.5 × IQR. It is robust for skewed data and resistant to small sample distortions.

Z-Score

The Z-score measures how many standard deviations an observation lies from the mean. Common thresholds are |z| > 3. Use with caution when data are non-normal.

Modified Z-Score (MAD-based)

Replaces the mean with the median and σ with Median Absolute Deviation (MAD): Modified Z = 0.6745 × (x – median) / MAD. Thresholds > 3.5 are typical.

Density & Clustering Methods

Algorithms such as DBSCAN, Isolation Forest, or Local Outlier Factor detect anomalous points in high-dimensional space without strong parametric assumptions.

Time-Series Approaches

For ordered data, techniques like STL decomposition residuals or ARIMA model residuals expose temporal anomalies.

A Practical Workflow

1. Data Profiling: Compute descriptive stats and visualize distributions (histograms, box plots).
2. Select a Method: Choose IQR for quick wins, Z-score for Gaussian data, isolation forests for multi-feature problems, etc.
3. Parameter Tuning: Adjust thresholds (e.g., 1.5 × IQR vs. 3 × IQR) based on domain tolerance for extremes.
4. Flag & Review: Mark suspected outliers and review them with domain experts.
5. Decide on Action: Options include correcting, capping/winsorizing, excluding, or modeling separately.

Best Practices

• Combine visual and statistical methods—plots often reveal patterns statistics miss.
• Keep raw data immutable; store flags in a separate column.
• Document your thresholds so future readers know why points were excluded.
• Automate with data-quality checks in your pipelines, triggering alerts when anomaly counts spike.

Example: Detecting Outliers with SQL in Galaxy

Below is a query you could run in the Galaxy SQL editor to flag unusually high order values in an orders table using the IQR method:

WITH stats AS (
SELECT
percentile_cont(0.25) WITHIN GROUP (ORDER BY total_amount) AS q1,
percentile_cont(0.75) WITHIN GROUP (ORDER BY total_amount) AS q3
FROM orders
), bounds AS (
SELECT
q1,
q3,
(q3 - q1) * 1.5 AS iqr
FROM stats
)
SELECT o.order_id,
o.total_amount,
CASE WHEN o.total_amount < q1 - iqr OR o.total_amount > q3 + iqr THEN 'OUTLIER' END AS outlier_flag
FROM orders o
CROSS JOIN bounds;


Galaxy’s AI copilot can autogenerate this query from a natural-language prompt such as “Find orders whose total amount is an outlier.” You can then save the statement to a Collection titled Data Quality Checks and share it with teammates for review and endorsement.

Common Mistakes

1. Using Z-score on skewed data – If your distribution is heavy-tailed, prefer IQR or MAD-based methods.
2. One-size-fits-all thresholds – Business tolerance varies; customize cut-offs in consultation with stakeholders.
3. Deleting suspected outliers without investigation – Extreme points can signal legitimate and valuable phenomena (e.g., viral sales days).

Conclusion

Outlier detection is not merely a statistical exercise—it’s a critical quality-assurance step that preserves the integrity of analytic insights. By combining robust statistical methods, domain knowledge, and tooling such as Galaxy’s collaborative SQL editor, teams can surface anomalies early and act on them with confidence.