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Distribution

Histogram

Groups numeric observations into intervals and shows frequency, proportion or density.

  • Plot
  • Distribution
  • Frequency

What it answers

Where do numeric observations concentrate?

Shape
Adjacent bars across numeric bins
What it shows
Centre, spread, skew and possible modes.

Example

The question

How long do customers usually take to pay an invoice?

Days to pay, 60 invoices (illustrative)

Days to pay, 60 invoices (illustrative)Illustrative data for 60 invoices. Days to pay in 10-day bins. 0 to 10: 2; 10 to 20: 5; 20 to 30: 14; 30 to 40: 16; 40 to 50: 10; 50 to 60: 6; 60 to 70: 3; 70 to 80: 2; 80 to 90: 1; 90 to 100: 1.Invoices04812160102030405060708090100Days to pay2514161063211
Days to pay, 60 invoices (illustrative)Illustrative data for 60 invoices. Days to pay in 10-day bins. 0 to 10: 2; 10 to 20: 5; 20 to 30: 14; 30 to 40: 16; 40 to 50: 10; 50 to 60: 6; 60 to 70: 3; 70 to 80: 2; 80 to 90: 1; 90 to 100: 1.Invoices0481216020406080100Days to pay2514161063211
Illustrative data for 60 invoices. Days to pay in 10-day bins. 0 to 10: 2; 10 to 20: 5; 20 to 30: 14; 30 to 40: 16; 40 to 50: 10; 50 to 60: 6; 60 to 70: 3; 70 to 80: 2; 80 to 90: 1; 90 to 100: 1.

Illustrative teaching data. It belongs to no company.

The misleading version

Same question, drawn badly

Two bins hide the shape

Two bins hide the shape0 to 50 days: 47 invoices. 50 to 100 days: 13 invoices.Invoices010203040500255075100Days to pay4713
Two bins hide the shape0 to 50 days: 47 invoices. 50 to 100 days: 13 invoices.Invoices010203040500255075100Days to pay4713
0 to 50 days: 47 invoices. 50 to 100 days: 13 invoices.

What goes wrong. With only two bins the peak at 30 to 40 days and the long tail vanish. Try several bin widths before you choose one.

When to use it

Use it when

Understanding one numeric distribution.

Avoid it when

When categories are discrete labels or bin choices dominate a tiny sample.

How to read it

Read numeric bin boundaries and bar frequency or density.

Note

Specify bins and the vertical-axis measure.

The data you need

Layout
Long table
Fields
Value: number
One row is
One raw numeric observation. What is the grain?
Rules
Use raw observations unless the chosen renderer explicitly accepts summaries or bins. Define missing-value treatment.
Optional
Optional frequency Weight. Pre-binned alternative: BinStart,BinEnd,Count; do not treat counts as raw values.
Preparation
Keep raw numeric observations, group them if needed and handle missingness explicitly. Choose bins, bandwidth or summary conventions as appropriate to this specific plot.
Units
The measurement axis retains its original unit. Frequency uses observations; normalised frequency divides by group sample size; density also depends on interval width.
Example of the input layout: Long tableA long table: one row per observation, few columns. New data adds rows.ValueAABBC
A long table: one row per observation, few columns. New data adds rows.

Worked example

Values 10,12,12,18,23; bins[10,15),[15,20),[20,25). Counts 3,1,1 show concentration in the first bin.

Value
10
12
12
18
23
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