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ASTM D6299-17a

(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
5.1 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.  
5.2 Data accrued, using the techniques included in this practice, provide the ability to monitor analytical measurement system precision and bias.  
5.3 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.  
1.7 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.

General Information

Status
Historical
Publication Date
14-Nov-2017
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 D6299-17 - Standard Practice for Applying Statistical Quality Assurance and Control Charting Techniques to Evaluate Analytical Measurement System Performance
Effective Date
15-Nov-2017
Replaced By
ASTM D6299-17b - Standard Practice for Applying Statistical Quality Assurance and Control Charting Techniques to Evaluate Analytical Measurement System Performance
Effective Date
15-Dec-2017
Referred By
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
15-Nov-2017
Referred By
ASTM D7620-10(2020) - Standard Test Method for Determination of Total Sulfur in Liquid Hydrocarbon Based Fuels by Continuous Injection, Air Oxidation and Ultraviolet Fluorescence Detection
Effective Date
15-Nov-2017
Referred By
ASTM D6278-20a - Standard Test Method for Shear Stability of Polymer Containing Fluids Using a European Diesel Injector Apparatus
Effective Date
15-Nov-2017
Referred By
ASTM D4057-22 - Standard Practice for Manual Sampling of Petroleum and Petroleum Products
Effective Date
15-Nov-2017
Referred By
ASTM D8428-21 - Standard Guide for Establishing Analyst Competence to Perform a Test Method
Effective Date
15-Nov-2017
Referred By
ASTM D7875-20 - Standard Test Method for Determination of Butanol and Acetone Content of Butanol for Blending with Gasoline by Gas Chromatography
Effective Date
15-Nov-2017
Referred By
ASTM D5186-22 - Standard Test Method for Determination of the Aromatic Content and Polynuclear Aromatic Content of Diesel Fuels By Supercritical Fluid Chromatography
Effective Date
15-Nov-2017
Referred By
ASTM D7214-23 - Standard Test Method for Determination of the Oxidation of Used Lubricants by FT-IR Using Peak Area Increase Calculation
Effective Date
15-Nov-2017
Referred By
ASTM F2980-13(2017) - Standard Test Method for Analysis of Heavy Metals in Glass by Field Portable X-Ray Fluorescence (XRF)
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15-Nov-2017
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ASTM D7923-19 - Standard Test Method for Water in Ethanol and Hydrocarbon Blends by Karl Fischer Titration
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Referred By
ASTM D7861-14(2019) - Standard Test Method for Determination of Fatty Acid Methyl Esters (FAME) in Diesel Fuel by Linear Variable Filter (LVF) Array Based Mid-Infrared Spectroscopy
Effective Date
15-Nov-2017
Referred By
ASTM D3764-23 - Standard Practice for Validation of the Performance of Process Stream Analyzer Systems
Effective Date
15-Nov-2017
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ASTM D7279-20 - Standard Test Method for Kinematic Viscosity of Transparent and Opaque Liquids by Automated Houillon Viscometer
Effective Date
15-Nov-2017

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ASTM D6299-17a - Standard Practice for Applying Statistical Quality Assurance and Control Charting Techniques to Evaluate Analytical Measurement System PerformanceASTM D6299-17a - 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
Designation: D6299 − 17a An American National Standard
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.Anumber in parentheses indicates the year of last reapproval.A
superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
further guidance and information.
1. Scope*
1.7 This international standard was developed in accor-
1.1 This practice covers information for the design and
dance with internationally recognized principles on standard-
operationofaprogramtomonitorandcontrolongoingstability
ization established in the Decision on Principles for the
and precision and bias performance of selected analytical
Development of International Standards, Guides and Recom-
measurement systems using a collection of generally accepted
mendations issued by the World Trade Organization Technical
statistical quality control (SQC) procedures and tools.
Barriers to Trade (TBT) Committee.
NOTE 1—Acomplete list of criteria for selecting measurement systems
towhichthispracticeshouldbeappliedandfordeterminingthefrequency
2. Referenced Documents
at which it should be applied is beyond the scope of this practice.
2
2.1 ASTM Standards:
However, some factors to be considered include (1) frequency of use of
the analytical measurement system, (2) criticality of the parameter being D3764PracticeforValidationofthePerformanceofProcess
measured, (3) system stability and precision performance based on
Stream Analyzer Systems
historical data, (4) business economics, and (5) regulatory, contractual, or
D5191Test Method for Vapor Pressure of Petroleum Prod-
test method requirements.
ucts (Mini Method)
1.2 This practice is applicable to stable analytical measure-
D6708Practice for StatisticalAssessment and Improvement
ment systems that produce results on a continuous numerical
of Expected Agreement Between Two Test Methods that
scale.
Purport to Measure the Same Property of a Material
D6792Practice for Quality Management Systems in Petro-
1.3 This practice is applicable to laboratory test methods.
leum Products, Liquid Fuels, and Lubricants Testing
1.4 This practice is applicable to validated process stream
Laboratories
analyzers.
D7372Guide for Analysis and Interpretation of Proficiency
1.5 This practice is applicable to monitoring the differences
Test Program Results
between two analytical measurement systems that purport to
E177Practice for Use of the Terms Precision and Bias in
measure the same property provided that both systems have
ASTM Test Methods
beenassessedinaccordancewiththestatisticalmethodologyin
E178Practice for Dealing With Outlying Observations
Practice D6708 and the appropriate bias applied.
E456Terminology Relating to Quality and Statistics
NOTE2—Forvalidationofunivariateprocessstreamanalyzers,seealso
E691Practice for Conducting an Interlaboratory Study to
Practice D3764.
Determine the Precision of a Test Method
NOTE 3—One or both of the analytical systems in 1.5 can be laboratory
test methods or validated process stream analyzers.
3. Terminology
1.6 This practice assumes that the normal (Gaussian) model
3.1 Definitions:
is adequate for the description and prediction of measurement
3.1.1 accepted reference value, n—a value that serves as an
system behavior when it is in a state of statistical control.
agreed-uponreferenceforcomparisonandthatisderivedas(1)
NOTE 4—For non-Gaussian processes, transformations of test results
atheoreticalorestablishedvalue,basedonscientificprinciples,
may permit proper application of these tools. Consult a statistician for
(2) an assigned value, based on experimental work of some
national or international organization, such as the U.S. Na-
tional Institute of Standards and Technology (NIST), or (3)a
1
This practice is under the jurisdiction ofASTM Committee D02 on Petroleum
Products, Liquid Fuels, and Lubricants and is the direct responsibility of Subcom-
2
mittee D02.94 on Coordinating Subcommittee on QualityAssurance and Statistics. For referenced ASTM standards, visit the ASTM website, www.astm.org, or
Current edition approved Nov. 15, 2017. Published November 2017. Originally contact ASTM Customer Service at service@astm.org. For Annual Book of ASTM
approved in 1998. Last previous edition approved in 2017 as D6299–17. DOI: Standards volume information, refer to the standard’s Document Summary page on
10.1520/D6299-17A. the ASTM website.
*A Summary of Changes section appears at the end of this standard
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. U
...

This document is not an ASTM standard and is intended only to provide the user of an ASTM 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.
Designation: D6299 − 17 D6299 − 17a An American National Standard
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.
1.7 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.
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 Management Systems in Petroleum Products, Liquid Fuels, 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
1
This practice is under the jurisdiction of ASTM Committee D02 on Petroleum Products, Liquid Fuels, and Lubricants and is the direct responsibility of Subcommittee
D02.94 on Coordinating Subcommittee on Quality Assurance and Statistics.
Current edition approved Jan. 1, 2017Nov. 15, 2017. Published January 2017November 2017. Originally approved in 1998. Last previous edition approved in 20162017
as D6299 – 16.D6299 – 17. DOI: 10.1520/D6299-17.10.1520/D6299-17A.
2
For referenced ASTM standards, visit the ASTM website, www.astm.org, or contact ASTM Customer Service at service@astm.org. For Annual Book of ASTM Standards
volume information, refer to the standard’s Document Summary page on the ASTM website.
*A Summary of Changes section appears at the end of this standard
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
1

---------------------- Page: 1 ----------------------
D6299 − 17a
E456 Terminology Relating to Quality and Statistics
E691
...

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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.  
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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 covers regression analysis of a set of data to define the statistical relationship between two numerical variables for use in predicting one variable from the other. This practice is restricted in scope to consider only a single numerical response variable and a single numerical predictor variable. The objective is to obtain a regression model for use in predicting the value of the response variable Y for given values of the predictor variable X.
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SCOPE
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ABSTRACT
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SIGNIFICANCE AND USE
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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.  
5.1.1 The initial response from a nondestructive evaluation inspection system is ultimately binary in nature (that is, hit or miss).  
5.1.2 Discontinuity size is the predictor variable and can be accurately quantified.  
5.1.3 A relationship between discontinuity size and POD exists and is best described by a generalized linear model with the appropriate link function for binary outcomes.  
5.2 This practice does not limit the use of a generalized linear model with more than one predictor variable or other types of statistical models if justified as more appropriate for the hit/miss data.  
5.3 If the initial response from a nondestructive evaluation inspection system is measurable and can be classified as a continuous variable (for example, data collected from an Eddy Current inspection system), then Practice E3023 may be more appropriate.  
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1.1 This practice covers the procedure for performing a statistical analysis on nondestructive testing hit/miss data to determine the demonstrated probability of detection (POD) for a specific set of examination parameters. Topics covered include the standard hit/miss POD curve formulation, validation techniques, and correct interpretation of results.  
1.2 The values stated in inch-pound units are to be regarded as standard. The values given in parentheses are mathematical conversions to SI units that are provided for information only and are not considered standard.  
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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 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 E2334-09(2023)(Practice)
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 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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