A boxplot is a simple chart that shows how a set of numerical data is distributed.

What a boxplot shows

• Median: The line inside the box — the middle value of the data.
• Box: Shows the interquartile range (IQR), from the 25th percentile (Q1) to the 75th percentile (Q3). This is where the central half of the data lies.
• Whiskers: Lines extending from the box to show the range of the data that is not considered an outlier.
• Outliers: Points plotted individually beyond the whiskers — unusual or extreme values.

Why use a boxplot?

• To quickly compare distributions across categories.
• To spot outliers.
• To understand data spread and skewness at a glance.

How to add a boxplot chart?

To build a boxplot, you must define at least five statistical measures from your dataset. These five values describe the distribution.

Requied measures :

• min

min(measure field)

• 25th percentile (Q1)

quantileExact(0.25)(measure field)

• Median (Q2)

quantileExact(0.50)(measure field)

• 75th percentile (Q3)

quantileExact(0.75)(measure field)

• Max

max(measure field)

These values are enough to draw the core boxplot

If you want your boxplot to display outliers, you can calculate them using the standard 1.5 × IQR rule:

• IQR (Interquartile Range) = Q3 − Q1
• Outliers are any values:
• below Q1 − 1.5 × IQR
• above Q3 + 1.5 × IQR

Formula:

arrayFilter(

    x -> x  (quantileExact(0.75)(measure field) + 1.5 * (quantileExact(0.75)(measure field) - quantileExact(0.25)(measure field))),

    groupArray(measure field)

)

This returns all the values considered outliers so they can be plotted individually.

Once your measures are created:

1. Add each calculated measure to your chart.
2. Open the Boxplot tab for each measure.
3. Assign the correct role (Min, Q1, Median, Q3, Max, Outliers).