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ASTM D6299-10

(Practice)

Standard Practice for Applying Statistical Quality Assurance and Control Charting Techniques to Evaluate Analytical Measurement System Performance

Standard Practice for Applying Statistical Quality Assurance and Control Charting Techniques to Evaluate Analytical Measurement System Performance

SIGNIFICANCE AND USE
This practice can be used to continuously demonstrate the proficiency of analytical measurement systems that are used for establishing and ensuring the quality of petroleum and petroleum products.
Data accrued, using the techniques included in this practice, provide the ability to monitor analytical measurement system precision and bias.
These data are useful for updating test methods as well as for indicating areas of potential measurement system improvement.
SCOPE
1.1 This practice covers information for the design and operation of a program to monitor and control ongoing stability and precision and bias performance of selected analytical measurement systems using a collection of generally accepted statistical quality control (SQC) procedures and tools.  
Note 1—A complete list of criteria for selecting measurement systems to which this practice should be applied and for determining the frequency at which it should be applied is beyond the scope of this practice. However, some factors to be considered include (1) frequency of use of the analytical measurement system, (2) criticality of the parameter being measured, (3) system stability and precision performance based on historical data, (4) business economics, and (5) regulatory, contractual, or test method requirements.
1.2 This practice is applicable to stable analytical measurement systems that produce results on a continuous numerical scale.  
1.3 This practice is applicable to laboratory test methods.
1.4 This practice is applicable to validated process stream analyzers.
1.5 This practice is applicable to monitoring the differences between two analytical measurement systems that purport to measure the same property provided that both systems have been assessed in accordance with the statistical methodology in Practice D6708 and the appropriate bias applied.
Note 2—For validation of univariate process stream analyzers, see also Practice D3764.
Note 3—One or both of the analytical systems in 1.5 can be laboratory test methods or validated process stream analyzers.
1.6 This practice assumes that the normal (Gaussian) model is adequate for the description and prediction of measurement system behavior when it is in a state of statistical control.
Note 4—For non-Gaussian processes, transformations of test results may permit proper application of these tools. Consult a statistician for further guidance and information.

General Information

Status
Historical
Publication Date
28-Feb-2010
ICS
03.120.30 - Application of statistical methods
Technical Committee
D02 - Petroleum Products, Liquid Fuels, and Lubricants
Drafting Committee
D02.94 - Coordinating Subcommittee on Quality Assurance and Statistics
Current Stage
Ref Project

Relations

Replaces
ASTM D6299-09 - Standard Practice for Applying Statistical Quality Assurance and Control Charting Techniques to Evaluate Analytical Measurement System Performance
Effective Date
01-Mar-2010
Referred By
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ASTM D7691-23 - Standard Test Method for Multielement Analysis of Crude Oils Using Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES)
Effective Date
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Effective Date
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Effective Date
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ASTM D8473-22 - Standard Test Method for Determining the Biobased content of Liquid Hydrocarbon Fuels Using Liquid Scintillation Counting with Spiked Carbon-14
Effective Date
01-Mar-2010
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ASTM D7109-22e1 - Standard Test Method for Shear Stability of Polymer-Containing Fluids Using a European Diesel Injector Apparatus at 30 Cycles and 90 Cycles
Effective Date
01-Mar-2010
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ASTM D7740-20 - Standard Practice for Optimization, Calibration, and Validation of Atomic Absorption Spectrometry for Metal Analysis of Petroleum Products and Lubricants
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01-Mar-2010
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ASTM D1318-23 - Standard Test Method for Sodium in Residual Fuel Oil (Flame Photometric Method)
Effective Date
01-Mar-2010
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ASTM D7040-04(2020) - Standard Test Method for Determination of Low Levels of Phosphorus in ILSAC GF 4 and Similar Grade Engine Oils by Inductively Coupled Plasma Atomic Emission Spectrometry
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01-Mar-2010
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ASTM D8174-18 - Standard Test Method for Finite Flash Point Determination of Liquid Wastes by Small-Scale Closed Cup Tester
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ASTM D6890-22 - Standard Test Method for Determination of Ignition Delay and Derived Cetane Number (DCN) of Diesel Fuel Oils by Combustion in a Constant Volume Chamber
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ASTM D7314-21 - Standard Practice for Determination of the Heating Value of Gaseous Fuels using Calorimetry and On-line/At-line Sampling
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ASTM D6897-16 - Standard Test Method for Vapor Pressure of Liquefied Petroleum Gases (LPG) (Expansion Method)
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ASTM D3231-18 - Standard Test Method for Phosphorus in Gasoline
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ASTM D6299-10 - Standard Practice for Applying Statistical Quality Assurance and Control Charting Techniques to Evaluate Analytical Measurement System PerformanceASTM D6299-10 - Standard Practice for Applying Statistical Quality Assurance and Control Charting Techniques to Evaluate Analytical Measurement System Performance
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Standards Content (Sample)

NOTICE: This standard has either been superseded and replaced by a new version or withdrawn.
Contact ASTM International (www.astm.org) for the latest information
An American National Standard
Designation:D6299–10
Standard Practice for
Applying Statistical Quality Assurance and Control Charting
Techniques to Evaluate Analytical Measurement System
1
Performance
This standard is issued under the fixed designation D6299; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. A
superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
1. Scope 2. Referenced Documents
2
1.1 This practice covers information for the design and 2.1 ASTM Standards:
operationofaprogramtomonitorandcontrolongoingstability D3764 Practice for Validation of the Performance of Pro-
and precision and bias performance of selected analytical cess Stream Analyzer Systems
measurement systems using a collection of generally accepted D5191 Test Method for Vapor Pressure of Petroleum Prod-
statistical quality control (SQC) procedures and tools. ucts (Mini Method)
D6708 PracticeforStatisticalAssessmentandImprovement
NOTE 1—Acomplete list of criteria for selecting measurement systems
of Expected Agreement Between Two Test Methods that
towhichthispracticeshouldbeappliedandfordeterminingthefrequency
Purport to Measure the Same Property of a Material
at which it should be applied is beyond the scope of this practice.
D6792 Practice for Quality System in Petroleum Products
However, some factors to be considered include (1) frequency of use of
the analytical measurement system, (2) criticality of the parameter being
and Lubricants Testing Laboratories
measured, (3) system stability and precision performance based on
D7372 Guide forAnalysis and Interpretation of Proficiency
historical data, (4) business economics, and (5) regulatory, contractual, or
Test Program Results
test method requirements.
E177 Practice for Use of the Terms Precision and Bias in
1.2 This practice is applicable to stable analytical measure-
ASTM Test Methods
ment systems that produce results on a continuous numerical
E178 Practice for Dealing With Outlying Observations
scale.
E456 Terminology Relating to Quality and Statistics
1.3 This practice is applicable to laboratory test methods.
E691 Practice for Conducting an Interlaboratory Study to
1.4 This practice is applicable to validated process stream
Determine the Precision of a Test Method
analyzers.
3. Terminology
1.5 This practice is applicable to monitoring the differences
between two analytical measurement systems that purport to
3.1 Definitions:
measure the same property provided that both systems have
3.1.1 accepted reference value, n—a value that serves as an
beenassessedinaccordancewiththestatisticalmethodologyin
agreed-uponreferenceforcomparisonandthatisderivedas(1)
Practice D6708 and the appropriate bias applied.
atheoreticalorestablishedvalue,basedonscientificprinciples,
(2) an assigned value, based on experimental work of some
NOTE 2—Forvalidationofunivariateprocessstreamanalyzers,seealso
national or international organization, such as the U.S. Na-
Practice D3764.
tional Institute of Standards and Technology (NIST), or (3)a
NOTE 3—One or both of the analytical systems in 1.5 can be laboratory
test methods or validated process stream analyzers.
consensus value, based on collaborative experimental work
under the auspices of a scientific or engineering group.
1.6 This practice assumes that the normal (Gaussian) model
E177, E456
is adequate for the description and prediction of measurement
3.1.2 accuracy, n—the closeness of agreement between an
system behavior when it is in a state of statistical control.
observed value and an accepted reference value. E177, E456
NOTE 4—For non-Gaussian processes, transformations of test results
3.1.3 assignable cause, n—a factor that contributes to
may permit proper application of these tools. Consult a statistician for
variation and that is feasible to detect and identify. E456
further guidance and information.
3.1.4 bias, n—a systematic error that contributes to the
difference between a population mean of the measurements or
1
This practice is under the jurisdiction ofASTM Committee D02 on Petroleum
Products and Lubricants and is the direct responsibility of Subcommittee D02.94 on
2
Coordinating Subcommittee on Quality Assurance and Statistics. For referenced ASTM standards, visit the ASTM website, www.astm.org, or
Current edition approved March 1, 2010. Published June 2010. Originally contact ASTM Customer Service at service@astm.org. For Annual Book of ASTM
approved in 1998. Last previous edition approved in 2009 as D6299–09. DOI: Standards volume information, refer to the standard’s Document Summary page on
10.1520/D6299-10. the ASTM website.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C7
...

This document is not anASTM standard and is intended only to provide the user of anASTM standard an indication of what changes have been made to the previous version. Because
it may not be technically possible to adequately depict all changes accurately, ASTM recommends that users consult prior editions as appropriate. In all cases only the current version
of the standard as published by ASTM is to be considered the official document.
An American National Standard
Designation:D6299–09 Designation:D6299–10
Standard Practice for
Applying Statistical Quality Assurance and Control Charting
Techniques to Evaluate Analytical Measurement System
1
Performance
This standard is issued under the fixed designation D6299; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. A
superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
1. Scope
1.1 This practice covers information for the design and operation of a program to monitor and control ongoing stability and
precision and bias performance of selected analytical measurement systems using a collection of generally accepted statistical
quality control (SQC) procedures and tools.
NOTE 1—A complete list of criteria for selecting measurement systems to which this practice should be applied and for determining the frequency at
which it should be applied is beyond the scope of this practice. However, some factors to be considered include (1) frequency of use of the analytical
measurement system, (2) criticality of the parameter being measured, (3) system stability and precision performance based on historical data, (4) business
economics, and (5) regulatory, contractual, or test method requirements.
1.2 This practice is applicable to stable analytical measurement systems that produce results on a continuous numerical scale.
1.3 This practice is applicable to laboratory test methods.
1.4 This practice is applicable to validated process stream analyzers.
1.5 This practice is applicable to monitoring the differences between two analytical measurement systems that purport to
measure the same property provided that both systems have been assessed in accordance with the statistical methodology in
Practice D6708 and the appropriate bias applied.
NOTE 2—For validation of univariate process stream analyzers, see also Practice D3764.
NOTE 3—One or both of the analytical systems in 1.5 can be laboratory test methods or validated process stream analyzers.
1.6 This practice assumes that the normal (Gaussian) model is adequate for the description and prediction of measurement
system behavior when it is in a state of statistical control.
NOTE 4—For non-Gaussian processes, transformations of test results may permit proper application of these tools. Consult a statistician for further
guidance and information.
2. Referenced Documents
2
2.1 ASTM Standards:
D3764 Practice for Validation of the Performance of Process Stream Analyzer Systems
D5191 Test Method for Vapor Pressure of Petroleum Products (Mini Method)
D6708 Practice for Statistical Assessment and Improvement of Expected Agreement Between Two Test Methods that Purport
to Measure the Same Property of a Material
D6792 Practice for Quality System in Petroleum Products and Lubricants Testing Laboratories
D7372 Guide for Analysis and Interpretation of Proficiency Test Program Results
E177 Practice for Use of the Terms Precision and Bias in ASTM Test Methods
E178 Practice for Dealing With Outlying Observations
E456 Terminology Relating to Quality and Statistics
E691 Practice for Conducting an Interlaboratory Study to Determine the Precision of a Test Method
3. Terminology
3.1 Definitions:
1
This practice is under the jurisdiction of ASTM Committee D02 on Petroleum Products and Lubricants and is the direct responsibility of Subcommittee D02.94 on
Coordinating Subcommittee on Quality Assurance and Statistics.
Current edition approved Nov.March 1, 2009.2010. Published FebruaryJune 2010. Originally approved in 1998. Last previous edition approved in 20082009 as
D6299–08.D6299–09. DOI: 10.1520/D6299-109.
2
For referencedASTM standards, visit theASTM website, www.astm.org, or contactASTM Customer Service at service@astm.org. For Annual Book ofASTM Standards
volume information, refer to the standard’s Document Summary page on the ASTM website.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.
1

---------------------- Page: 1 ----------------------
D6299–10
3.1.1 accepted reference value, n—a value that serves as an agreed-upon reference for comparison and that is derived as (1)a
theoretical or established value, based on scientific principles, (2) an assigned value, based on experimental work of some national
or international organization, such as the U.S. National Institute of Standards and
...

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SIGNIFICANCE AND USE
3.1 The GESD procedure can be used to simultaneously identify up to a pre-determined number of outliers (r) in a data set, without having to pre-examine the data set and make a priori decisions as to the location and number of potential outliers.  
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SCOPE
1.1 This practice provides a step by step procedure for the application of the Generalized Extreme Studentized Deviate (GESD) Many-Outlier Procedure to simultaneously identify multiple outliers in a data set. (See Bibliography.)  
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This practice presents rules for accepting or rejecting evidence based on a sample. Statistical evidence for this practice is in the form of an estimate of a proportion, an average, a total, or other numerical characteristic of a finite population or lot. This practice is an estimate of the result which would have been obtained by investigating the entire lot or population under the same rules and with the same care as was used for the sample. One purpose of this practice is to describe straightforward sample selection and data calculation procedures so that courts, commissions, etc. will be able to verify whether such procedures have been applied.
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4.5 Refer to Section 7 to guide in the interpretation of the uncertainty in the reported value of the parameter estimate, θ^, that is, the value of its standard error, se(θ^). The quantity se(θ^) should be reviewed to verify that the risks it entails are commensurate with the size of the sample.  
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ABSTRACT
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SIGNIFICANCE AND USE
5.1 The POD analysis method described herein is based on a well-known and well established statistical regression method. It shall be used to quantify the demonstrated POD for a specific set of examination parameters and known range of discontinuity sizes under the following conditions.  
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Standard Practice for Setting an Upper Confidence Bound for a Fraction or Number of Non-Conforming items, or a Rate of Occurrence for Non-Conformities, Using Attribute Data, When There is a Zero Response in the Sample

ABSTRACT
This practice presents methodology for the setting of an upper confidence bound regarding an unknown fraction or quantity non-conforming, or a rate of occurrence for nonconformities, in cases where the method of attributes is used and there is a zero response in a sample. Three cases are considered. In Case 1, the sample is selected from a process or a very large population of interest. In Case 2, a sample of n items is selected at random from a finite lot of N items. In Case 3, there is a process, but the output is a continuum, such as area (for example, a roll of paper or other material, a field of crop), volume (for example, a volume of liquid or gas), or time (for example, hours, days, quarterly, etc.) The sample size is defined as that portion of the �continuum� sampled, and the defined attribute may occur any number of times over the sampled portion.
SIGNIFICANCE AND USE
4.1 In Case 1, the sample is selected from a process or a very large population of interest. The population is essentially unlimited, and each item either has or has not the defined attribute. The population (process) has an unknown fraction of items p (long run average process non-conforming) having the attribute. The sample is a group of n discrete items selected at random from the process or population under consideration, and the attribute is not exhibited in the sample. The objective is to determine an upper confidence bound, pu, for the unknown fraction p whereby one can claim that p ≤ pu with some confidence coefficient (probability) C. The binomial distribution is the sampling distribution in this case.  
4.2 In Case 2, a sample of n items is selected at random from a finite lot of N items. Like Case 1, each item either has or has not the defined attribute, and the population has an unknown number, D, of items having the attribute. The sample does not exhibit the attribute. The objective is to determine an upper confidence bound, Du, for the unknown number D, whereby one can claim that D ≤ Du with some confidence coefficient (probability) C. The hypergeometric distribution is the sampling distribution in this case.  
4.3 In Case 3, there is a process, but the output is a continuum, such as area (for example, a roll of paper or other material, a field of crop), volume (for example, a volume of liquid or gas), or time (for example, hours, days, quarterly, etc.) The sample size is defined as that portion of the “continuum” sampled, and the defined attribute may occur any number of times over the sampled portion. There is an unknown average rate of occurrence, λ, for the defined attribute over the sampled interval of the continuum that is of interest. The sample does not exhibit the attribute. For a roll of paper, this might be blemishes per 100 ft2; for a volume of liquid, microbes per cubic litre; for a field of crop, spores per acre; for a time interval, ca...
SCOPE
1.1 This practice presents methodology for the setting of an upper confidence bound regarding a unknown fraction or quantity non-conforming, or a rate of occurrence for nonconformities, in cases where the method of attributes is used and there is a zero response in a sample. Three cases are considered.  
1.1.1 The sample is selected from a process or a very large population of discrete items, and the number of non-conforming items in the sample is zero.  
1.1.2 A sample of items is selected at random from a finite lot of discrete items, and the number of non-conforming items in the sample is zero.  
1.1.3 The sample is a portion of a continuum (time, space, volume, area, etc.) and the number of non-conformities in the sample is zero.  
1.2 Allowance is made for misclassification error in this practice, but only when misclassification rates are well understood or known and can be approximated numerically.  
1.3 The values stated in inch-pound units are to be regarded as standard. No other units of measurement are included in this standard.  
1.4 This ...

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ASTM
ASTM E1970-23(Practice)
Standard Practice for Statistical Treatment of Thermoanalytical Data

SIGNIFICANCE AND USE
5.1 The standard deviation, or one of its derivatives, such as relative standard deviation or pooled standard deviation, derived from this practice, provides an estimate of precision in a measured value. Such results are ordinarily expressed as the mean value ± the standard deviation, that is, X ± s.  
5.2 If the measured values are, in the statistical sense, “normally” distributed about their mean, then the meaning of the standard deviation is that there is a 67 % chance, that is 2 in 3, that a given value will lie within the range of ± one standard deviation of the mean value. Similarly, there is a 95 % chance, that is 19 in 20, that a given value will lie within the range of ± two standard deviations of the mean. The two standard deviation range is sometimes used as a test for outlying measurements.  
5.3 The calculation of precision in the slope and intercept of a line, derived from experimental data, commonly is required in the determination of kinetic parameters, vapor pressure or enthalpy of vaporization. This practice describes how to obtain these and other statistically derived values associated with measurements by thermal analysis.
SCOPE
1.1 This practice details the statistical data treatment used in some thermal analysis methods.  
1.2 The method describes the commonly encountered statistical tools of the mean, standard derivation, relative standard deviation, pooled standard deviation, pooled relative standard deviation, the best fit to a (linear regression of a) straight line (or plane), and propagation of uncertainties for all calculations encountered in thermal analysis methods (see Practice E2586).  
1.3 Some thermal analysis methods derive the analytical value from the slope or intercept of a linear regression straight line (or plane) assigned to three or more sets of data pairs. Such methods may require an estimation of the precision in the determined slope or intercept. The determination of this precision is not a common statistical tool. This practice details the process for obtaining such information about precision.  
1.4 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, health, and environmental practices and determine the applicability of regulatory limitations prior to use.  
1.5 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.

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ASTM E1994-09(2023)(Practice)
Standard Practice for Use of Process Oriented AOQL and LTPD Sampling Plans

ABSTRACT
This practice is primarily a statement of principals for the guidance of ASTM technical committees and others in the use of average outgoing quality limit, AOQL, and lot tolerance percent defective, LTPD, sampling plans for determining acceptable of lots of product. Two general types of tables are given, one based on the concept of lot tolerance, LTPD, and the other on AOQL. For each of the types, tables are provided both for single sampling and for double sampling. Each of the individual tables constitutes a collection of solutions to the problem of minimizing the over-all amount of inspection.
SIGNIFICANCE AND USE
4.1 Two general types of tables (Note 1) are given, one based on the concept of lot tolerance, LTPD, and the other on AOQL. The broad conditions under which the different types have been found best adapted are indicated below.  
4.1.1 For each of the types, tables are provided both for single sampling and for double sampling. Each of the individual tables constitutes a collection of solutions to the problem of minimizing the over-all amount of inspection. Because each line in the tables covers a range of lot sizes, the AOQL values in the LTPD tables and the LTPD values in the AOQL tables are often conservative.
Note 1: Tables in Annex A1 – Annex A4 and parts of the text are reproduced by permission of John R. Wiley and Sons. More extensive tables and discussion of the methods will be found in that text.  
4.2 The sampling tables based on lot quality protection (LTPD) (the tables in Annex A1 and Annex A2) are perhaps best adapted to conditions where interest centers on each lot separately, for example, where the individual lot tends to retain its identity either from a shipment or a service standpoint. These tables have been found particularly useful in inspections made by the ultimate consumer or a purchasing agent for lots or shipments purchased more or less intermittently.  
4.3 The sampling tables based on average quality protection (AOQL) (the tables in Annex A3 and Annex A4) are especially adapted for use where interest centers on the average quality of product after inspection rather than on the quality of each individual lot and where inspection is, therefore, intended to serve, if necessary, as a partial screen for defective pieces. The latter point of view has been found particularly helpful, for example, in consumer inspections of continuing purchases of large quantities of a product and in manufacturing process inspections of parts where the inspection lots tend to lose their identity by merger in a common storeroom from which quantities are withdraw...
SCOPE
1.1 This practice is primarily a statement of principals for the guidance of ASTM technical committees and others in the use of average outgoing quality limit, AOQL, and lot tolerance percent defective, LTPD, sampling plans for determining acceptable of lots of product.  
1.2 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.

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ASTM D7915-22(Practice)
Standard Practice for Application of Generalized Extreme Studentized Deviate (GESD) Technique to Simultaneously Identify Multiple Outliers in a Data Set

SIGNIFICANCE AND USE
3.1 The GESD procedure can be used to simultaneously identify up to a pre-determined number of outliers (r) in a data set, without having to pre-examine the data set and make a priori decisions as to the location and number of potential outliers.  
3.2 The GESD procedure is robust to masking. Masking describes the phenomenon where the existence of multiple outliers can prevent an outlier identification procedure from declaring any of the observations in a data set to be outliers.  
3.3 The GESD procedure is automation-friendly, and hence can easily be programmed as automated computer algorithms.
SCOPE
1.1 This practice provides a step by step procedure for the application of the Generalized Extreme Studentized Deviate (GESD) Many-Outlier Procedure to simultaneously identify multiple outliers in a data set. (See Bibliography.)  
1.2 This practice is applicable to a data set comprising observations that is represented on a continuous numerical scale.  
1.3 This practice is applicable to a data set comprising a minimum of six observations.  
1.4 This practice is applicable to a data set where the normal (Gaussian) model is reasonably adequate for the distributional representation of the observations in the data set.  
1.5 The probability of false identification of outliers associated with the decision criteria set by this practice is 0.01.  
1.6 It is recommended that the execution of this practice be conducted under the guidance of personnel familiar with the statistical principles and assumptions associated with the GESD technique.  
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, health, and environmental practices and determine the applicability of regulatory limitations prior to use.  
1.8 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.

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ASTM E122-17(2022)(Practice)
Standard Practice for Calculating Sample Size to Estimate, With Specified Precision, the Average for a Characteristic of a Lot or Process

ABSTRACT
This practice covers simple methods for calculating how many units to include in a random sample in order to estimate with a specified precision, a measure of quality for all the units of a lot of material or produced by a process. It also treats the common situation where the sampling units can be considered to exhibit a single source of variability; it does not treat multi-level sources of variability.
SIGNIFICANCE AND USE
4.1 This practice is intended for use in determining the sample size required to estimate, with specified precision, a measure of quality of a lot or process. The practice applies when quality is expressed as either the lot average for a given property, or as the lot fraction not conforming to prescribed standards. The level of a characteristic may often be taken as an indication of the quality of a material. If so, an estimate of the average value of that characteristic or of the fraction of the observed values that do not conform to a specification for that characteristic becomes a measure of quality with respect to that characteristic. This practice is intended for use in determining the sample size required to estimate, with specified precision, such a measure of the quality of a lot or process either as an average value or as a fraction not conforming to a specified value.
SCOPE
1.1 This practice covers simple methods for calculating how many units to include in a random sample in order to estimate with a specified precision, a measure of quality for all the units of a lot of material, or produced by a process. This practice will clearly indicate the sample size required to estimate the average value of some property or the fraction of nonconforming items produced by a production process during the time interval covered by the random sample. If the process is not in a state of statistical control, the result will not have predictive value for immediate (future) production. The practice treats the common situation where the sampling units can be considered to exhibit a single (overall) source of variability; it does not treat multi-level sources of variability.  
1.2 The system of units for this standard is not specified. Dimensional quantities in the standard are presented only as illustrations of calculation methods. The examples are not binding on products or test methods treated.  
1.3 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.

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