Summarize data distributions with Boxplots (Box-and-Whisker). Visualize median, quartiles, and outliers for robust statistical comparison.

Boxplots (or Box-and-Whisker plots) are the standard for summarizing the distribution of data groups. They display the Median, Quartiles, and Outliers in a compact format, facilitating comparison between categories (e.g., "Salary range" across "Departments"). While less common in basic dashboards, they are indispensable for data analysts and statisticians.

Edilitics brings this advanced statistical chart to the web with a point-and-click interface, automatically calculating the five-number summary from your governed datasets.

When to Use a Boxplot

Use Case	Why This Chart Works
Salary Comparison	Compare salary bands across departments, revealing equality/inequality.
Performance Variability	Analyze test scores or load times: Consistently clustered or wildly varying?
Outlier Identify	Instantly spot data points that fall statistically outside the "normal" range.
A/B Testing	Scientifically compare the distribution of outcomes from two different strategies.

Chart Configuration in Edilitics

Inputs Required

Data Type	Required Count	Description
Dimensions (Columns)	1	Categorical field for the X-Axis.
Metrics (Rows)	1	Numerical field used to calculate distribution stats (min, q1, median, q3, max).

How to Configure a Boxplot

Select "Boxplot" from the Chart Library.
Assign Data:
- Drag your grouping dimension to Category (X-Axis).
- Drag your metric to Value (Y-Axis).
Calculation:
- Edilitics automatically scans the raw data for each category and calculates:
  - Min/Max (Whiskers)
  - Q1/Q3 (Box edges)
  - Median (Line inside box)
  - Outliers (Individual points beyond whiskers).
Formatting:
- Customize box width and colors to match your dashboard theme.

Feature Highlights

Automatic Statistical Computation

No need to pre-calculate quartiles in SQL. Pass the raw data, and the visualization engine runs the statistics in the browser.

Outlier Visualization

Points that fall beyond 1.5x IQR (Inter-Quartile Range) are plotted as individual dots, highlighting anomalies that skew averages.

Multi-Series Support

Group multiple boxplots side-by-side (e.g., "Male" vs "Female" scores within "Classroom A").

Interactive Tooltips

Hovering over a box provides a clear summary: "Upper Quartile: X", "Median: Y", "Lower Quartile: Z".

Best Practices for Boxplots

Practice	Why It Matters
Educate Your Audience	Not everyone knows how to read a boxplot. Include a "Help" note explaining the box/whiskers.
Use for Comparison	A single boxplot is boring; the value comes from comparing the alignment of multiple boxes.
Sort by Median	Sorting categories by their median value creates a clear "performance ranking" visual.
Raw Data Required	Boxplots need the granular rows to calculate distribution; do not feed them pre-aggregated "sums".

How Edilitics Is Different

Unlike specialized statistical software (R, Python), Edilitics integrates boxplots directly into your BI dashboard:

Unified View: Place a "Sales Revenue" KPI next to a "Order Variance" Boxplot - executive summary and analyst depth in one view.
Automatic Calculations: Min, Q1, Median, Q3, Max, and outliers are computed automatically from granular data.
No Code Required: Statistical visualization without writing scripts or formulas.
Filter-Responsive: Apply dashboard filters to see how distribution changes across segments.

This brings statistical depth to business users without requiring technical expertise.

Boxplots answer the question "Is the average telling the truth?". They expose the variance and consistency behind the headline numbers, providing a deeper layer of truth.

Need help? Email support@edilitics.com with your workspace, job ID, and context. We reply within one business day.

Boxplots for Statistical Distribution