Standard Guide for
Estimating the Magnitude of Variability from Expected
Sources in Sampling Plans1
This standard is issued under the fixed designation D 4854; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of the last revision. A number in parentheses indicates the year of last reapproval. A
superscript epsilon (e) indicates an editorial change since the last revision or reapproval.
1.1 This guide serves as an aid to subcommittees in writing
specifications and sampling procedures.
1.2 The guide explains how to estimate the contributions of
the variability of lot sampling units, laboratory sampling units,
and specimens to the variation of the test result of a sampling
1.3 The guide explains how to combine the estimates of the
variability from the three sources to obtain an estimate of the
variability of the sampling plan results.
1.4 The guide is applicable to all sampling plans that
produce variables data (Note 1). It is not applicable to plans
that produce attribute data since such plans do not take
specimens in stages, but require that specimens be taken at
random from all of the individual items in the lot.
NOTE 1—This guide is applicable to all sampling plans that produce
variables data regardless of the kind of frequency distribution of these
data, because no estimates are made of any probabilities.
1.5 This guide includes the following topics:
Topic Title Section
Referenced Documents 2
Significance and Use 4
Sampling Plans Producing Variables Data 5
Reducing Variability of Sampling Results 6
Analysis of Data Using ANOVA Annex A1
A Numerical Example Annex A2
2. Referenced Documents
2.1 ASTM Standards:
D 123 Terminology Relating to Textiles2
D 2904 Practice for Interlaboratory Testing of a Textile Test
The method that Produces Normally Distributed Data2
D 4271 Practice for Writing Statements on Sampling in Test
Methods for Textiles3
D 4467 Practice for Interlaboratory Testing of a Textile Test
The method that Produces Non-Normally Distributed Data3
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