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Spread Parameter Standard Deviation

Standard deviation is the square root of variance. Sample standard deviation uses the n − 1 variance denominator and is expressed in the same unit as the observations. It summarises numerical spread around the mean.

Expected output: A labelled spread estimate with units and intended use made clear.

01 · Purpose

Why and when to use it

Use it alongside location and charts, and in models whose assumptions justify a standard-deviation-based interpretation.

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. Check the population, units and variance convention.
  2. Calculate variance from squared deviations.
  3. Take its square root.
  4. State whether the estimate is overall or a within-subgroup process estimate.
  5. Inspect the distribution and time behaviour before using normal-model claims.

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.

Sample 2, 4, 4, 6, 9 minutesResult
Sample variance7 min²
Sample standard deviation√7 = 2.646 minutes
Mean ± one sample SD5 ± 2.646 minutes; descriptive interval, not a confidence interval

04 · Interpretation

What the result tells you

A standard deviation has the original unit. Roughly 68% within one standard deviation is a normal-distribution property, not a universal rule for any dataset.

Completion check: A labelled spread estimate with units and intended use made clear.

05 · Judgement

Mistakes and limitations

The ordinary sample SD is not automatically the within-process sigma required for Cp/Cpk. Outliers, dependence and mixtures can change what the estimate represents.

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.