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ASTM D6299-02

(Practice)

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

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

SCOPE
1.1 This practice provides 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.
Note 2—For validation of univariate process stream analyzers, see also Practice D 3764.
1.5 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 3—For non-Gaussian processes, transformations of test results may permit proper application of these tools. Consult a statistician for further guidance and information.
1.6 This practice does not address statistical techniques for comparing two or more analytical measurement systems applying different analytical techniques or equipment components that purport to measure the same property(s).

General Information

Status
Historical
Publication Date
09-Jun-2002
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

Replaced By
ASTM D6299-02e1 - Standard Practice for Applying Statistical Quality Assurance Techniques to Evaluate Analytical Measurement System Performance
Effective Date
10-Jun-2002
Referred By
ASTM D7757-22 - Standard Test Method for Silicon in Gasoline and Related Products by Monochromatic Wavelength Dispersive X-ray Fluorescence Spectrometry
Effective Date
10-Jun-2002
Referred By
ASTM D6708-21 - Standard Practice for Statistical Assessment and Improvement of Expected Agreement Between Two Test Methods that Purport to Measure the Same Property of a Material
Effective Date
10-Jun-2002
Referred By
ASTM D5291-21 - Standard Test Methods for Instrumental Determination of Carbon, Hydrogen, and Nitrogen in Petroleum Products and Lubricants
Effective Date
10-Jun-2002
Referred By
ASTM D3246-15 - Standard Test Method for Sulfur in Petroleum Gas by Oxidative Microcoulometry
Effective Date
10-Jun-2002
Referred By
ASTM D5453-19a - Standard Test Method for Determination of Total Sulfur in Light Hydrocarbons, Spark Ignition Engine Fuel, Diesel Engine Fuel, and Engine Oil by Ultraviolet Fluorescence
Effective Date
10-Jun-2002
Referred By
ASTM D4927-20 - Standard Test Methods for Elemental Analysis of Lubricant and Additive Components—Barium, Calcium, Phosphorus, Sulfur, and Zinc by Wavelength-Dispersive X-Ray Fluorescence Spectroscopy
Effective Date
10-Jun-2002
Referred By
ASTM E3146-20 - Standard Test Method for Determination of Carbonyls in Pyrolysis Bio-Oils by Potentiometric Titration
Effective Date
10-Jun-2002
Referred By
ASTM D8274-20a - Standard Test Method for Determination of Biodiesel (Fatty Acid Methyl Esters) Content in Diesel Fuel Oil by Portable Rapid Mid-Infrared Analyzer
Effective Date
10-Jun-2002
Referred By
ASTM D6792-23a - Standard Practice for Quality Management Systems in Petroleum Products, Liquid Fuels, and Lubricants Testing Laboratories
Effective Date
10-Jun-2002
Referred By
ASTM D4294-21 - Standard Test Method for Sulfur in Petroleum and Petroleum Products by Energy Dispersive X-ray Fluorescence Spectrometry
Effective Date
10-Jun-2002
Referred By
ASTM D4629-17 - Standard Test Method for Trace Nitrogen in Liquid Hydrocarbons by Syringe/Inlet Oxidative Combustion and Chemiluminescence Detection
Effective Date
10-Jun-2002
Referred By
ASTM D3831-22 - Standard Test Method for Manganese in Gasoline By Atomic Absorption Spectroscopy
Effective Date
10-Jun-2002
Referred By
ASTM D1318-23 - Standard Test Method for Sodium in Residual Fuel Oil (Flame Photometric Method)
Effective Date
10-Jun-2002
Referred By
ASTM F2853-10(2015) - Standard Test Method for Determination of Lead in Paint Layers and Similar Coatings or in Substrates and Homogenous Materials by Energy Dispersive X-Ray Fluorescence Spectrometry Using Multiple Monochromatic Excitation Beams
Effective Date
10-Jun-2002

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ASTM D6299-02 - Standard Practice for Applying Statistical Quality Assurance Techniques to Evaluate Analytical Measurement System PerformanceASTM D6299-02 - Standard Practice for Applying Statistical Quality Assurance Techniques to Evaluate Analytical Measurement System Performance
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ASTM D6299-02 - Standard Practice for Applying Statistical Quality Assurance 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:D6299–02
Standard Practice for
Applying Statistical Quality Assurance Techniques to
1
Evaluate Analytical Measurement System Performance
This standard is issued under the fixed designation D 6299; 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 (e) indicates an editorial change since the last revision or reapproval.
1. Scope D 3764 Practice for Validation of Process Stream Analyzer
2
Systems
1.1 This practice provides information for the design and
D 5191 Test Method for Vapor Pressure of Petroleum Prod-
operationofaprogramtomonitorandcontrolongoingstability
2
ucts (Mini Method)
and precision and bias performance of selected analytical
E 177 Practice for Use of the Terms Precision and Bias in
measurement systems using a collection of generally accepted
3
ASTM Test Methods
statistical quality control (SQC) procedures and tools.
3
E 178 Practice for Dealing with Outlying Observations
NOTE 1—Acomplete list of criteria for selecting measurement systems 3
E 456 Terminology Relating to Quality and Statistics
towhichthispracticeshouldbeappliedandfordeterminingthefrequency
E 691 Practice for Conducting an Interlaboratory Study to
at which it should be applied is beyond the scope of this practice.
3
Determine the Precision of a Test Method
However, some factors to be considered include (1) frequency of use of
the analytical measurement system, (2) criticality of the parameter being
3. Terminology
measured, (3) system stability and precision performance based on
historical data, (4) business economics, and (5) regulatory, contractual, or 3.1 Definitions:
test method requirements.
3.1.1 accepted reference value, n—a value that serves as an
agreed-uponreferenceforcomparisonandthatisderivedas(1)
1.2 This practice is applicable to stable analytical measure-
atheoreticalorestablishedvalue,basedonscientificprinciples,
ment systems that produce results on a continuous numerical
(2) an assigned value, based on experimental work of some
scale.
national or international organization, such as the U.S. Na-
1.3 This practice is applicable to laboratory test methods.
tional Institute of Standards and Technology (NIST), or (3)a
1.4 This practice is applicable to validated process stream
consensus value, based on collaborative experimental work
analyzers.
under the auspices of a scientific or engineering group.
NOTE 2—Forvalidationofunivariateprocessstreamanalyzers,seealso
(E 456/E 177)
Practice D 3764.
3.1.2 accuracy, n—the closeness of agreement between an
1.5 This practice assumes that the normal (Gaussian) model
observed value and an accepted reference value. (E 456/
is adequate for the description and prediction of measurement
E 177)
system behavior when it is in a state of statistical control.
3.1.3 assignable cause, n—a factor that contributes to
variation and that is feasible to detect and identify. (E 456)
NOTE 3—For non-Gaussian processes, transformations of test results
3.1.4 bias, n—a systematic error that contributes to the
may permit proper application of these tools. Consult a statistician for
further guidance and information.
difference between a population mean of the measurements or
test results and an accepted reference or true value. (E 456/
1.6 This practice does not address statistical techniques for
E 177)
comparing two or more analytical measurement systems ap-
3.1.5 control limits, n—limits on a control chart that are
plying different analytical techniques or equipment compo-
used as criteria for signaling the need for action or for judging
nents that purport to measure the same property(s).
whether a set of data does or does not indicate a state of
2. Referenced Documents
statistical control. (E 456)
3.1.6 lot, n—a definite quantity of a product or material
2.1 ASTM Standards:
accumulated under conditions that are considered uniform for
sampling purposes. (E 456)
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
Quality Assurance and Statistics.
2
Current edition approved June 10, 2002. Published September 2002. Originally Annual Book of ASTM Standards, Vol 05.02.
3
published as D 6299–98. Last previous edition D 6299–00. Annual Book of ASTM Standards, Vol 14.02.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.
1

---------------------- Page: 1 ----------------------
D6299–02
3.1.7 precision, n—the closeness of agreement between test 3.2.8.1 Discussion—ASTM Committee D02 conducts pro-
results obtained under prescribed conditions. (E 456) ficiency testing a
...

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4.3.4 Section 8 di...
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Standard Practice for Demonstrating Capability to Comply with an Acceptance Procedure

ABSTRACT
This practice provides a general methodology for evaluating single-stage or multiple-stage acceptance procedures which involve a quality characteristic measured on a numerical scale. This methodology computes, at a prescribed confidence level, a lower bound on the probability of passing an acceptance procedure, using estimates of the parameters of the distribution of test results from a sampled population.
SIGNIFICANCE AND USE
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ABSTRACT
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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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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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.  
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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.
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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.  
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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...
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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.  
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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 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
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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