ASTM D6044-96(2003)
(Guide)Standard Guide for Representative Sampling for Management of Waste and Contaminated Media
Standard Guide for Representative Sampling for Management of Waste and Contaminated Media
SIGNIFICANCE AND USE
Representative samples are defined in the context of the study objectives.
This guide defines the meaning of a representative sample, as well as the attributes the sample(s) needs to have in order to provide a valid inference from the sample data to the population.
This guide also provides a process to identify the sources of error (both systematic and random) so that an effort can be made to control or minimize these errors. These sources include sampling error, measurement error, and statistical bias.
When the objective is limited to the taking of a representative (physical) sample or a representative set of (physical) samples, only potential sampling errors need to be considered. When the objective is to make an inference from the sample data to the population, additional measurement error and statistical bias need to be considered.
This guide does not apply to the cases where the taking of a nonrepresentative sample(s) is prescribed by the study objective. In that case, sampling approaches such as judgment sampling or biased sampling can be taken. These approaches are not within the scope of this guide.
Following this guide does not guarantee that representative samples will be obtained. But failure to follow this guide will likely result in obtaining sample data that are either biased or imprecise, or both. Following this guide should increase the level of confidence in making the inference from the sample data to the population.
This guide can be used in conjunction with the DQO process (see Practice D 5792).
This guide is intended for those who manage, design, and implement sampling and analytical plans for waste management and contaminated media.
SCOPE
1.1 This guide covers the definition of representativeness in environmental sampling, identifies sources that can affect representativeness (especially bias), and describes the attributes that a representative sample or a representative set of samples should possess. For convenience, the term "representative sample" is used in this guide to denote both a representative sample and a representative set of samples, unless otherwise qualified in the text.
1.2 This guide outlines a process by which a representative sample may be obtained from a population. The purpose of the representative sample is to provide information about a statistical parameter(s) (such as mean) of the population regarding some characteristic(s) (such as concentration) of its constituent(s) (such as lead). This process includes the following stages: (1) minimization of sampling bias and optimization of precision while taking the physical samples, (2) minimization of measurement bias and optimization of precision when analyzing the physical samples to obtain data, and (3) minimization of statistical bias when making inference from the sample data to the population. While both bias and precision are covered in this guide, major emphasis is given to bias reduction.
1.3 This guide describes the attributes of a representative sample and presents a general methodology for obtaining representative samples. It does not, however, provide specific or comprehensive sampling procedures. It is the user's responsibility to ensure that proper and adequate procedures are used.
1.4 The assessment of the representativeness of a sample is not covered in this guide since it is not possible to ever know the true value of the poplulation.
1.5 Since the purpose of each sampling event is unique, this guide does not attempt to give a step by step account of how to develop a sampling design that results in the collection of representative samples.
1.6 Appendix X1 contains two case studies, which discuss the factors for obtaining representative samples.
1.7 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health practices and ...
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Designation:D6044–96 (Reapproved 2003)
Standard Guide for
Representative Sampling for Management of Waste and
Contaminated Media
This standard is issued under the fixed designation D6044; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision.Anumber in parentheses indicates the year of last reapproval.A
superscript epsilon (e) indicates an editorial change since the last revision or reapproval.
1. Scope 1.7 This standard does not purport to address all of the
safety concerns, if any, associated with its use. It is the
1.1 This guide covers the definition of representativeness in
responsibility of the user of this standard to establish appro-
environmental sampling, identifies sources that can affect
priate safety and health practices and determine the applica-
representativeness (especially bias), and describes the at-
bility of regulatory limitations prior to use.
tributes that a representative sample or a representative set of
samples should possess. For convenience, the term“ represen-
2. Referenced Documents
tative sample” is used in this guide to denote both a represen-
2.1 ASTM Standards:
tative sample and a representative set of samples, unless
D3370 PracticesforSamplingWaterfromClosedConduits
otherwise qualified in the text.
D4448 GuideforSamplingGroundwaterMonitoringWells
1.2 This guide outlines a process by which a representative
D4547 Practice for Sampling Waste and Soils for Volatile
samplemaybeobtainedfromapopulation.Thepurposeofthe
Organic Compounds
representative sample is to provide information about a statis-
D4700 Guide for Soil Sampling from the Vadose Zone
tical parameter(s) (such as mean) of the population regarding
D4823 Guide for Core Sampling Submerged, Unconsoli-
some characteristic(s) (such as concentration) of its constitu-
dated Sediments
ent(s) (such as lead). This process includes the following
D5088 Practice for Decontamination of Field Equipment
stages: (1) minimization of sampling bias and optimization of
Used at Nonradioactive Waste Sites
precision while taking the physical samples, (2) minimization
D5792 Practice for Generation of Environmental Data
of measurement bias and optimization of precision when
Related to Waste ManagementActivities: Development of
analyzing the physical samples to obtain data, and (3) minimi-
Data Quality Objectives
zation of statistical bias when making inference from the
D5956 Guide for Sampling Strategies for Heterogeneous
sample data to the population. While both bias and precision
Wastes
are covered in this guide, major emphasis is given to bias
D6051 Guide for Composite Sampling and Field Subsam-
reduction.
pling for Environmental Waste Management Activities
1.3 This guide describes the attributes of a representative
sample and presents a general methodology for obtaining
3. Terminology
representative samples. It does not, however, provide specific
3.1 analytical unit, n—the actual amount of the sample
or comprehensive sampling procedures. It is the user’s respon-
material analyzed in the laboratory.
sibilitytoensurethatproperandadequateproceduresareused.
3.2 bias, n—a systematic positive or negative deviation of
1.4 The assessment of the representativeness of a sample is
the sample or estimated value from the true population value.
not covered in this guide since it is not possible to ever know
3.2.1 Discussion—This guide discusses three sources of
the true value of the population.
bias—sampling bias, measurement bias, and statistical bias.
1.5 Sincethepurposeofeachsamplingeventisunique,this
There is a sampling bias when the value inherent in the
guidedoesnotattempttogiveastepbystepaccountofhowto
physical samples is systematically different from what is
develop a sampling design that results in the collection of
inherent in the population.
representative samples.
There is a measurement bias when the measurement process
1.6 Appendix X1 contains two case studies, which discuss
produces a sample value systematically different from that
the factors for obtaining representative samples.
inherent in the sample itself, although the physical sample is
This guide is under the jurisdiction of ASTM Committee D34 on Waste
Management and is the direct responsibility of Subcommittee D34.01.01 on For referenced ASTM standards, visit the ASTM website, www.astm.org, or
Planning for Sampling. contact ASTM Customer Service at service@astm.org. For Annual Book of ASTM
Current edition approved March 10, 2003. Published June 2003. Originally Standards volume information, refer to the standard’s Document Summary page on
approved in 1996. Last previous edition approved in 1996 as D6044–96. the ASTM website.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.
D6044–96 (2003)
itself unbiased. Measurement bias can also include any sys- presence of fundamental heterogeneity (or fundamental error)
tematic difference between the original sample and the sample in the population that sampling variance arises. Degree of
analyzed, when the analyzed sample may have been altered samplingvariancedefinesthedegreeofprecisioninestimating
due to improper procedures such as improper sample preser- the population parameter using the sample data. The smaller
vation or preparation, or both. the sampling variance is, the more precise the estimate is. See
also sampling error.
There is a statistical bias when, in the absence of sampling
3.11 homogeneity, n—theconditionofthepopulationunder
biasandmeasurementbias,thestatisticalprocedureproducesa
which all items of the population are identical with respect to
biased estimate of the population value.
the characteristic(s) of interest.
Sampling bias is considered the most important factor
3.12 judgment sampling, n—taking of a sample(s) based on
affecting inference from the samples to the population.
judgment that it will more or less represent the average
3.3 biased sampling, n—thetakingofasample(s)withprior
condition of the population.
knowledge that the sampling result will be biased relative to
3.12.1 Discussion—The sampling location(s) is selected
the true value of the population.
because it is judged to be representative of the average
3.3.1 Discussion—This is the taking of a sample(s) based
condition of the population. It can be effective when the
on available information or knowledge, especially in terms of
populationisrelativelyhomogeneousorwhentheprofessional
visible signs or knowledge of contamination. This kind of
judgment is good. It may or may not introduce bias. It is a
sampling is used to detect the presence of localized contami-
usefulsamplingapproachwhenprecisionisnotaconcern.This
nation or to identify the source of a contamination. The
is one form of authoritative sampling (see biased sampling.)
sampling results are not intended for generalization to the
3.13 population, n—the totality of items or units of mate-
entire population. This is one form of authoritative sampling
rials under consideration.
(see judgment sampling.)
3.14 representative sample, n—asamplecollectedinsucha
3.4 characteristic, n—a property of items in a sample or
mannerthatitreflectsoneormorecharacteristicsofinterest(as
population that can be measured, counted, or otherwise ob-
defined by the project objectives) of a population from which
served, such as viscosity, flash point, or concentration.
it is collected.
3.5 composite sample, n—a combination of two or more
3.14.1 Discussion—Arepresentative sample can be a single
samples.
sample, a collection of samples, or one or more composite
samples.Asingle sample can be representative only when the
3.6 constituent, n—anelement,component,oringredientof
population is highly homogeneous.
the population.
3.15 representative sampling, n—the process of obtaining a
3.6.1 Discussion—Ifapopulationcontainsseveralcontami-
representative sample or a representative set of samples.
nants (such as acetone, lead, and chromium), these contami-
3.16 representative set of samples, n—a set of samples that
nants are called the constituents of the population.
collectively reflect one or more characteristics of interest of a
3.7 Data Quality Objectives, DQOs, n—qualitative and
population from which they were collected. See representative
quantitativestatementsderivedfromaDQOprocessdescribing
sample.
the decision rules and the uncertainties of the decision(s)
3.17 sample, n—a portion of material that is taken for
within the context of the problem(s) (see Practice D5792).
testing or for record purposes.
3.8 Data Quality Objective Process—aqualitymanagement
3.17.1 Discussion—Sample is a term with numerous mean-
tool based on the Scientific Method and developed by the U.S.
ings. The scientist collecting physical samples (for example,
Environmental Protection Agency to facilitate the planning of
fromalandfill,drum,ormonitoringwell)oranalyzingsamples
environmental data collection activities. The DQO process
considers a sample to be that unit of the population that was
enables planners to focus their planning efforts by specifying
collected and placed in a container. A statistician considers a
the use of data (the decision), the decision criteria (action
sample to be a subset of the population, and this subset may
level),andthedecisionmaker’sacceptabledecisionerrorrates.
consist of one or more physical samples. To minimize confu-
The products of the DQO process are the DQOs (see Practice
sion, the term sample, as used in this guide, is a reference to
D5792).
either a physical sample held in a sample container, or that
3.9 error, n—the random or systematic deviation of the
portion of the population that is subjected to in situ measure-
observed sample value from its true value (see bias and
ments, or a set of physical samples. See representative sample.
sampling error).
3.17.1.1 Theterm sample sizealsomeansdifferentthingsto
3.10 heterogeneity, n—the condition or degree of the popu- thescientistandthestatistician.Toavoidconfusion,termssuch
lation under which all items of the population are not identical as sample mass/sample volume and number of samples are
with respect to the characteristic(s) of interest. used instead of sample size.
3.10.1 Discussion—Although the ultimate interest is in the
statistical parameter such as the mean concentration of a
constituent of the population, heterogeneity relates to the
Pitard, F. F., “Pierre Gy’s Sampling Theory and Sampling Practice: Heteroge-
presence of differences in the characteristics (for example,
neity, Sampling Correctness and Statistical Process Control,” 2nd ed., CRC Press
concentration) of the units in the population. It is due to the Publishers, 1993.
D6044–96 (2003)
3.18 sampling error—thesystematicandrandomdeviations 4.8 This guide is intended for those who manage, design,
ofthesamplevaluefromthatofthepopulation.Thesystematic and implement sampling and analytical plans for waste man-
error is the sampling bias. The random error is the sampling agement and contaminated media.
variance.
5. Representative Samples
3.18.1 Discussion—Before the physical samples are taken,
5.1 Samples are taken to infer about some statistical param-
potential sampling variance comes from the inherent popula-
eter(s) of the population regarding some characteristic(s) of its
tion heterogeneity (sometimes called the “fundamental error,”
constituent(s) of interest. This is discussed in the following
see heterogeneity). In the physical sampling stage, additional
sections.
contributors to sampling variance include random errors in
5.2 Samples—When a representative sample consists of a
collectingthesamples.Afterthesamplesarecollected,another
single physical sample, it is a sample that by itself reflects the
contributor is the random error in the measurement process. In
characteristics of interest of the population. On the other hand,
each of these stages, systematic errors can occur as well, but
when a representative sample consists of a set of physical
they are the sources of bias, not sampling variance.
samples, the samples collectively reflect some characteristics
3.18.1.1 Samplingvarianceisoftenusedtorefertothetotal
of the population, though the samples individually may not be
variance from the various sources.
representative.Inmostcases,morethanonephysicalsampleis
3.19 stratum, n—a subgroup of the population separated in
necessary to characterize the population, because the popula-
space or time, or both, from the remainder of the population,
tion in environmental sampling is usually heterogeneous.
beinginternallysimilarwithrespecttoatargetcharacteristicof
5.3 Constituents and Characteristics—A population can
interest, and different from adjacent strata of the population.
possess many constituents, each with many characteristics.
3.19.1 Discussion—A landfill may display spatially sepa-
Usually it is only a subset of these constituents and character-
rated strata, such as old cells containing different wastes than
istics that are of interest in the context of the stated problem.
new cells. A waste pipe may discharge into temporally sepa-
Therefore, samples need to be representative of the population
rated strata of different constituents or concentrations, or both,
only in terms of these constituent(s) and characteristic(s) of
ifnight-shiftproductionvariesfromthedayshift.Inthisguide,
interest. A sampling plan needs to be designed accordingly.
strata refer mostly to the stratification in the concentrations of
5.4 Parameters—Similarly, samples need to be representa-
the same constituent(s).
tiveofthepopulationonlyintheparameter(s)ofinterest.Ifthe
3.20 subsample, n—a portion of the original sample that is
interestisonlyinestimatingaparametersuchasthepopulation
taken for testing or for record purposes.
mean, then composite samples, when taken correctly, will not
4. Significance and Use
be biased and therefore constitute a representative sample
4.1 Representative samples are defined in the context of the (regarding bias) for that parameter. On the other hand, if the
study objectives. interesthappenstobetheestimationofthepopulationvariance
4.2 This guide defines the meaning of a representative (of individual sampling units), another parameter, then the
sample, as well as the attributes the sample(s) needs to have in variance of the composite samples is a biased estimate of the
order to provide a valid inference from the sample data to the populationvarianceandthereforeisnotrepresentative.(Itisto
population. be noted that composite samples are often used to increase the
4.3 This guide also provides a process to identify the precisioninestimatingthepopulationmeanandnottoestimate
sources of error (both
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