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ASTM E 178 Document Information:
Title
Standard Practice for Dealing with Outlying Observations
ASTM International
Publication Date:
May 10, 2002
Scope:
This practice covers outlying observations in samples and how to test
the statistical significance
of them. An outlying observation, or "outlier," is one that appears to
deviate markedly from other
members of the sample in which it occurs. In this connection, the
following two alternatives are of
interest:
An outlying observation may be merely an extreme manifestation of the
random variability inherent
in the data. If this is true, the value should be retained and
processed in the same manner as the
other observations in the sample.
On the other hand, an outlying observation may be the result of gross
deviation from prescribed
experimental procedure or an error in calculating or recording the
numerical value. In such cases,
it may be desirable to institute an investigation to ascertain the
reason for the aberrant value.
The observation may even actually be rejected as a result of the
investigation, though not
necessarily so. At any rate, in subsequent data analysis the outlier
or outliers will be recognized
as probably being from a different population than that of the other
sample values.
It is our purpose here to provide statistical rules that will lead the
experimenter almost
unerringly to look for causes of outliers when they really exist, and
hence to decide whether
alternative 1.1.1 above, is not the more plausible hypothesis to
accept, as compared to alternative
1.1.2, in order that the most appropriate action in further data
analysis may be taken. The
procedures covered herein apply primarily to the simplest kind of
experimental data, that is,
replicate measurements of some property of a given material, or
observations in a supposedly single
random sample. Nevertheless, the tests suggested do cover a wide
enough range of cases in practice
to have broad utility.
Keywords:
- dixon test
- gross deviation
- Grubbs test
- outlier
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