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Probability Plot

A probability plot compares ordered observations with quantiles expected under a selected probability distribution. Approximate alignment with a reference line supports, but does not prove, the plausibility of that distribution model.

Expected output: A documented assessment of a candidate distribution’s plausibility.

01 · Purpose

Why and when to use it

Use it before analyses that rely on a distribution assumption, such as normal-model capability calculations.

Who takes part?

The measurement owner works with people who understand the process and the recorded data. Statistical or measurement-system decisions should involve someone able to check the assumptions and study design.

02 · Application

How to apply it

Start with an agreed measurement purpose, explicit units and trustworthy observations. Identify missing cases and assumptions before calculating.

  1. Choose a plausible distribution based on the characteristic and context.
  2. Sort observations and calculate plotting positions using a documented convention.
  3. Compare them with theoretical quantiles.
  4. Inspect curvature, tails, ties and outlying points.
  5. Combine the plot with process knowledge, sample size and appropriate model checks.

03 · Example

A worked measurement example

Original fictional teaching example. Small datasets illustrate the method; they are not proposed as sufficient study sizes. Supplied parameters are assumptions, not estimates from a real process.

Ordered value (minutes)Illustrative position (i − 0.5) / n
20.10
40.30
40.50
60.70
90.90
Theoretical normal quantileObserved minutes-1010510
Normal probability-plot construction for five values; no distribution conclusion from this tiny sample.

04 · Interpretation

What the result tells you

These positions specify where to compare each value with the chosen distribution’s quantiles. The five-value example teaches construction; it is insufficient to confidently establish tail behaviour.

Completion check: A documented assessment of a candidate distribution’s plausibility.

05 · Judgement

Mistakes and limitations

A straight-looking plot does not prove independence or stability. Non-significant normality tests may simply reflect low power; large samples can reveal practically minor deviations.

07 · Reading

Sources and further reading

The tool list follows the accepted LeanTrainingLab overview and its reference to Six Sigma+Lean Toolset, second edition (Lunau, editor; Springer, 2013). The book chapters were not available for direct review during this build. Explanations, examples and worksheets are original teaching material. Further reading supplies professional background; local conventions are stated explicitly.