iTeh StandardsiTeh Standards
EURUSD
Your shopping cart is empty.
ASTM

ASTM E3041-17

(Guide)

Standard Guide for Selecting and Using Scales for Sensory Evaluation

Standard Guide for Selecting and Using Scales for Sensory Evaluation

SIGNIFICANCE AND USE
4.1 Rating instruments or rating scales are commonly used in many areas such as sensory evaluation, marketing research, experimental psychology, survey research, and economics in which there is interest in quantifying perceptions such as liking, preference, level of purchase interest, intensity of an attribute, degree of difference, or level of agreement with statements. This guide is concerned with the scales that are used to record human responses to physical stimuli rather than measuring physical entities. Many types of rating scales already exist and have been used in the above fields. Specific rating scales each have their own properties, advantages, and disadvantages. Some rating scales are intended for specific applications, while others have broader applications. Some rating scales have been extensively studied and modeled and have well-established properties.  
4.2 Given the overwhelming number of scales available to practitioners when designing research, it is necessary for the researcher to have some knowledge about the scales that are available along with the many considerations that surround their use and applications. This guide will be useful to the sensory researcher who wants to use a scale as a measuring tool for their work. Selecting the right scale is a critical step towards meeting the research objective and making valid conclusions.
SCOPE
1.1 The objective of this guide is to provide information to be reviewed and considered by the sensory and consumer scientist who wants to select and use scales to measure responses from consumers or trained assessors. For ease of reading, the term sensory scientist is used throughout the guide when statements apply to the sensory and consumer scientists.  
1.2 This guide covers a brief definition of scales as well as some fundamental and practical challenges the sensory and consumer scientists should be aware of when using scales. It also provides a list and a description of the most commonly used scales in the field of sensory evaluation and consumer product research along with a classification framework for these scales.  
1.3 The scope of this guide is limited to the sensory and consumer science professional’s selection and use of rating scales when an assessor assigns one symbol/value to one stimulus, to their perception of a stimulus or an internal attitude/opinion. It does not cover:  
1.3.1 Details of analysis of data obtained from rating scales,  
1.3.2 Guidelines for questionnaire design including attribute selection,  
1.3.3 Fundamentals of measurement such as reliability and validity,  
1.3.4 Measurement models used to convert scale responses into measures of unobserved sensory or hedonic quantities,  
1.3.5 Tasks in which the assessor assigns a symbol/value to a group of stimuli,  
1.3.6 Rankings, and  
1.3.7 Multi-item scales.  
1.4 Units—The values stated in SI units are to be regarded as the standard. No other units of measurement are included in this standard.  
1.5 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.  
1.6 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
Published
Publication Date
30-Nov-2017
ICS
03.120.30 - Application of statistical methods
Technical Committee
E18 - Sensory Evaluation
Drafting Committee
E18.04 - Test Methods
Current Stage
Ref Project

Relations

Refers
ASTM E253-19 - Standard Terminology Relating to Sensory Evaluation of Materials and Products
Effective Date
15-Oct-2019
Refers
ASTM E253-18a - Standard Terminology Relating to Sensory Evaluation of Materials and Products
Effective Date
01-Oct-2018
Refers
ASTM E253-18 - Standard Terminology Relating to Sensory Evaluation of Materials and Products
Effective Date
15-Jun-2018
Refers
ASTM E253-17 - Standard Terminology Relating to Sensory Evaluation of Materials and Products
Effective Date
01-May-2017
Refers
ASTM E253-16 - Standard Terminology Relating to Sensory Evaluation of Materials and Products
Effective Date
01-Jun-2016
Refers
ASTM E253-15b - Standard Terminology Relating to Sensory Evaluation of Materials and Products
Effective Date
01-Dec-2015
Refers
ASTM E253-15a - Standard Terminology Relating to Sensory Evaluation of Materials and Products
Effective Date
01-Jun-2015
Refers
ASTM E253-15 - Standard Terminology Relating to Sensory Evaluation of Materials and Products
Effective Date
15-Jan-2015
Refers
ASTM E253-13a - Standard Terminology Relating to Sensory Evaluation of Materials and Products
Effective Date
01-Nov-2013
Refers
ASTM E253-13 - Standard Terminology Relating to Sensory Evaluation of Materials and Products
Effective Date
01-May-2013
Refers
ASTM E253-12a - Standard Terminology Relating to Sensory Evaluation of Materials and Products
Effective Date
01-Nov-2012
Refers
ASTM E253-12 - Standard Terminology Relating to Sensory Evaluation of Materials and Products
Effective Date
01-May-2012
Refers
ASTM E253-11a - Standard Terminology Relating to Sensory Evaluation of Materials and Products
Effective Date
01-Dec-2011
Refers
ASTM E2299-11 - Standard Guide for Sensory Evaluation of Products by Children
Effective Date
15-Nov-2011
Refers
ASTM E253-11 - Standard Terminology Relating to Sensory Evaluation of Materials and Products
Effective Date
15-Apr-2011

Buy Standard

ASTM E3041-17 - Standard Guide for Selecting and Using Scales for Sensory EvaluationASTM E3041-17 - Standard Guide for Selecting and Using Scales for Sensory Evaluation
PreviousNext
Guide
ASTM E3041-17 - Standard Guide for Selecting and Using Scales for Sensory Evaluation
English language
17 pages
sale 15% off
Preview
sale 15% off
Preview

Standards Content (Sample)

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.
Designation: E3041 − 17
Standard Guide for
1
Selecting and Using Scales for Sensory Evaluation
This standard is issued under the fixed designation E3041; 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 priate safety, health, and environmental practices and deter-
mine the applicability of regulatory limitations prior to use.
1.1 The objective of this guide is to provide information to
1.6 This international standard was developed in accor-
be reviewed and considered by the sensory and consumer
dance with internationally recognized principles on standard-
scientist who wants to select and use scales to measure
ization established in the Decision on Principles for the
responses from consumers or trained assessors. For ease of
Development of International Standards, Guides and Recom-
reading, the term sensory scientist is used throughout the guide
mendations issued by the World Trade Organization Technical
when statements apply to the sensory and consumer scientists.
Barriers to Trade (TBT) Committee.
1.2 This guide covers a brief definition of scales as well as
some fundamental and practical challenges the sensory and
2. Referenced Documents
consumer scientists should be aware of when using scales. It
2
2.1 ASTM Standards:
also provides a list and a description of the most commonly
E253 Terminology Relating to Sensory Evaluation of Mate-
used scales in the field of sensory evaluation and consumer
rials and Products
product research along with a classification framework for
E2299 Guide for Sensory Evaluation of Products by Chil-
these scales.
dren and Minors
1.3 The scope of this guide is limited to the sensory and
consumer science professional’s selection and use of rating 3. Terminology
scales when an assessor assigns one symbol/value to one
3.1 Definitions—See Terminology E253 for definitions re-
stimulus, to their perception of a stimulus or an internal
lating to sensory evaluation of materials and products.
attitude/opinion. It does not cover:
3.2 Definitions of Terms Specific to This Standard:
1.3.1 Details of analysis of data obtained from rating scales,
3.2.1 interval data—data obtained from a scale for which
1.3.2 Guidelinesforquestionnairedesignincludingattribute
numerically identical differences on any part of the scale
selection,
correspond to the same magnitude of difference.
1.3.3 Fundamentals of measurement such as reliability and
3.2.1.1 Discussion—The occurrence of a zero point in
validity,
interval data does not correspond to the complete absence of
1.3.4 Measurement models used to convert scale responses
the characteristic measured. An example of interval data is a
into measures of unobserved sensory or hedonic quantities,
temperatureindegreesFahrenheitwhereeachdegreechangeis
1.3.5 Tasks in which the assessor assigns a symbol/value to
thesamechangeinthermalheatregardlessofpointonthescale
a group of stimuli,
and 0°F does not represent the complete absence of thermal
1.3.6 Rankings, and
energy.
1.3.7 Multi-item scales.
3.2.2 ordered category scale, n—rating instrument in which
1.4 Units—The values stated in SI units are to be regarded
the categories used to encode the responses are ordered by
as the standard. No other units of measurement are included in
magnitude.
this standard.
3.2.3 ordinal data, n—data obtained when items are ordered
1.5 This standard does not purport to address all of the
with respect to magnitude, but the magnitudes of difference
safety concerns, if any, associated with its use. It is the
among successively ordered items are not necessarily equal.
responsibility of the user of this standard to establish appro-
3.2.3.1 Discussion—Examples include ranking, just-about-
right scales, and the Likert scale.
1
This guide is under the jurisdiction of ASTM Committee E18 on Sensory
2
Evaluation and is the direct responsibility of Subcommittee E18.04 on Fundamen- For referenced ASTM standards, visit the ASTM website, www.astm.org, or
tals of Sensory. contact ASTM Customer Service at service@astm.org. For Annual Book of ASTM
Current edition approved Dec. 1, 2017. Published January 2018. DOI: 10.1520/ Standards volume information, refer to the standard’s Document Summary page on
E3041-17. the ASTM website.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
1

---------------------- Page: 1 ----------------------
E3041 − 17
3.2.4 rating instrument, n—co
...

Questions, Comments and Discussion

Ask us and Technical Secretary will try to provide an answer. You can facilitate discussion about the standard in here.

This May Also Interest You

ASTM
ASTM E2709-23(Practice)
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
4.1 This practice considers inspection procedures that may involve multiple-stage sampling, where at each stage one can decide to accept or to continue sampling, and the decision to reject is deferred until the last stage.  
4.1.1 At each stage there are one or more acceptance criteria on the test results; for example, limits on each individual test result, or limits on statistics based on the sample of test results, such as the average, standard deviation, or coefficient of variation (relative standard deviation).  
4.2 The methodology in this practice defines an acceptance region for a set of test results from the sampled population such that, at a prescribed confidence level, the probability that a sample from the population will pass the acceptance procedure is greater than or equal to a prespecified lower bound.  
4.2.1 Having test results fall in the acceptance region is not equivalent to passing the acceptance procedure, but provides assurance that a sample would pass the acceptance procedure with a specified probability.  
4.2.2 This information can be used for process demonstration, validation of test methods, and qualification of instruments, processes, and materials.  
4.2.3 This information can be used for lot release (acceptance), but the lower bound may be conservative in some cases.  
4.2.4 If the results are to be applied to future test results from the same process, then it is assumed that the process is stable and predictable. If this is not the case then there can be no guarantee that the probability estimates would be valid predictions of future process performance.  
4.3 This methodology was originally developed (1-4)3 for use in two specific quality characteristics of drug products in the pharmaceutical industry but will be applicable for acceptance procedures in all industries.  
4.4 Mathematical derivations would be required that are specific to the individual criteria of each test.
SCOPE
1.1 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.  
1.2 For a prescribed lower probability bound, the methodology can also generate an acceptance limit table, which defines a set of test method outcomes (for example, sample averages and standard deviations) that would pass the acceptance procedure at a prescribed confidence level.  
1.3 This approach may be used for demonstrating compliance with in-process, validation, or lot-release specifications.  
1.4 The system of units for this practice is not specified.  
1.5 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.  
1.6 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.

  • Standard
    8 pages
    English language
    sale 15% off
  • Standard
    8 pages
    English language
    sale 15% off
ASTM
ASTM E141-10(2023)(Practice)
Standard Practice for Acceptance of Evidence Based on the Results of Probability Sampling

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.
SIGNIFICANCE AND USE
4.1 This practice is designed to permit users of sample survey data to judge the trustworthiness of results from such surveys. Practice E105 provides a statement of principles for guidance of ASTM technical committees and others in the preparation of a sampling plan for a specific material. Guide E1402 describes the principal types of sampling designs. Practice E122 aids in deciding on the required sample size.  
4.2 Section 5 gives extended definitions of the concepts basic to survey sampling and the user should verify that such concepts were indeed used and understood by those who conducted the survey. What was the frame? How large (exactly) was the quantity N? How was the parameter θ estimated and its standard error calculated? If replicate subsamples were not used, why not? Adequate answers should be given for all questions. There are many acceptable answers to the last question.  
4.3 If the sample design was relatively simple, such as simple random or stratified, then fully valid estimates of sampling variance are easily available. If a more complex design was used then methods such as discussed in Ref (1)3 or in Guide E1402 may be acceptable. Use of replicate subsamples is the most straightforward way to estimate sampling variances when the survey design is complex.  
4.4 Once the survey procedures that were used satisfy Section 5, see if any increase in sample size is needed. The calculations for making it objectively are described in Section 6.  
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.  
4.6 When the audit subsample shows that there was reasonable conformity with prescribed procedures and when the known instances of departures from the survey plan can be shown to have no appreciable effect on the est...
SCOPE
1.1 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. It 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.  
1.2 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. The methods may not give least uncertainty at least cost, they should however furnish a reasonable estimate with calculable uncertainty.  
1.3 This practice is primarily intended for one-of-a-kind studies. Repetitive surveys allow estimates of sampling uncertainties to be pooled; the emphasis of this practice is on estimation of sampling uncertainty from the sample itself. The parameter of interest for this practice is effectively a constant. Thus, the principal inference is a simple point estimate to be used as if it were the unknown constant, rather than, for example, a forecast or prediction interval or distribution...

  • Standard
    6 pages
    English language
    sale 15% off
ASTM
ASTM E3080-23(Practice)
Standard Practice for Regression Analysis with a Single Predictor Variable

ABSTRACT
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.
SIGNIFICANCE AND USE
4.1 Regression analysis is a procedure that uses data to study the statistical relationships between two or more variables (1, 2).3 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.  
4.2 A regression model consists of: (1) a regression function that relates the mean values of the response variable distribution to fixed values of the predictor variable, and (2) a statistical distribution that describes the variability in the response variable values at a fixed value of the predictor variable.  
4.2.1 The regression analysis utilizes either experimental or observational data to estimate the parameters defining a regression model and their precision. Diagnostic procedures are utilized to assess the resulting model fit and can suggest other models for improved prediction performance.  
4.3 The information in this practice is arranged as follows.  
4.3.1 Section 5 gives a general outline of the steps in the regression analysis procedure. The subsequent sections cover procedures for estimation of specific regression models.  
4.3.2 Section 6 assumes a straight line relationship between the two variables. This is also known as the simple linear regression model or a first order model. This model should be used as a starting point for understanding the XY relationship and ultimately defining the best fitting model to the data.  
4.3.3 Section 7 considers a proportional relationship between the variables, where the ratio of one variable to the other is constant. The intercept is constrained to be zero. This model is useful for single point calibration, where a reference material is run periodically as a standard during routine testing to correct for drift in instrument performance over a given range of test results.  
4.3.4 Section 8 di...
SCOPE
1.1 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.  
1.2 The regression analysis provides graphical and calculational procedures for selecting the best statistical model that describes the relationship and for evaluation of the fit of the data to the selected model.  
1.3 The resulting regression model can be useful for developing process knowledge through description of the variable relationship, in making predictions of future values, in relating the precision of a test method to the value of the characteristic being measured, and in developing control methods for the process generating values of the variables.  
1.4 The system of units for this practice is not specified. Dimensional quantities in the practice are presented only as illustrations of calculation methods. The examples are not binding on products or test methods treated.  
1.5 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.  
1.6 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development ...

  • Standard
    23 pages
    English language
    sale 15% off
  • Standard
    23 pages
    English language
    sale 15% off
ASTM
ASTM E2862-23(Practice)
Standard Practice for Probability of Detection Analysis for Hit/Miss Data

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.  
5.4 Prior to performing the analysis it is assumed that the discontinuity of interest is clearly defined; the number and distribution of induced discontinuity sizes in the POD specimen set is known and well-documented; discontinuities in the POD specimen set are unobstructed; and the POD examination administration procedure (including data collection method) is well-designed, well-defined, under control, and unbiased. The analysis results are only valid if convergence is achieved and the model adequately represents the data.  
5.5 The POD analysis method described herein is consistent with the analysis method for binary data described in MIL-HDBK-1823A, and is included in several widely utilized POD software packages to perform a POD analysis on hit/miss data. It is also found in statistical software packages that have generalized line...
SCOPE
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.  
1.3 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.  
1.4 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.

  • Standard
    14 pages
    English language
    sale 15% off
  • Standard
    14 pages
    English language
    sale 15% off
ASTM
ASTM D6299-23e1(Practice)
Standard Practice for Applying Statistical Quality Assurance and Control Charting Techniques to Evaluate Analytical Measurement System Performance

SIGNIFICANCE AND USE
5.1 This practice may 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.  
5.4 Control chart statistics can be used to compute limits that the signed difference (Δ) between two single results for the same sample obtained under site precision conditions is expected to fall outside of about 5 % of the time, when each result is obtained using a different measurement system in the same laboratory executing the same test method, and both systems are in a state of statistical control.
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 may 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.

  • Standard
    36 pages
    English language
    sale 15% off
ASTM
ASTM D6299-23(Practice)
Standard Practice for Applying Statistical Quality Assurance and Control Charting Techniques to Evaluate Analytical Measurement System Performance

SIGNIFICANCE AND USE
5.1 This practice may 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.  
5.4 Control chart statistics can be used to compute limits that the signed difference (Δ) between two single results for the same sample obtained under site precision conditions is expected to fall outside of about 5 % of the time, when each result is obtained using a different measurement system in the same laboratory executing the same test method, and both systems are in a state of statistical control.
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 may 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.

  • Standard
    36 pages
    English language
    sale 15% off
  • Standard
    36 pages
    English language
    sale 15% off
ASTM
ASTM E2709-19(2023)(Practice)
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
4.1 This practice considers inspection procedures that may involve multiple-stage sampling, where at each stage one can decide to accept or to continue sampling, and the decision to reject is deferred until the last stage.  
4.1.1 At each stage there are one or more acceptance criteria on the test results; for example, limits on each individual test result, or limits on statistics based on the sample of test results, such as the average, standard deviation, or coefficient of variation (relative standard deviation).  
4.2 The methodology in this practice defines an acceptance region for a set of test results from the sampled population such that, at a prescribed confidence level, the probability that a sample from the population will pass the acceptance procedure is greater than or equal to a prespecified lower bound.  
4.2.1 Having test results fall in the acceptance region is not equivalent to passing the acceptance procedure, but provides assurance that a sample would pass the acceptance procedure with a specified probability.  
4.2.2 This information can be used for process demonstration, validation of test methods, and qualification of instruments, processes, and materials.  
4.2.3 This information can be used for lot release (acceptance), but the lower bound may be conservative in some cases.  
4.2.4 If the results are to be applied to future test results from the same process, then it is assumed that the process is stable and predictable. If this is not the case then there can be no guarantee that the probability estimates would be valid predictions of future process performance.  
4.3 This methodology was originally developed (1-4)3 for use in two specific quality characteristics of drug products in the pharmaceutical industry but will be applicable for acceptance procedures in all industries.  
4.4 Mathematical derivations would be required that are specific to the individual criteria of each test.
SCOPE
1.1 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.  
1.2 For a prescribed lower probability bound, the methodology can also generate an acceptance limit table, which defines a set of test method outcomes (for example, sample averages and standard deviations) that would pass the acceptance procedure at a prescribed confidence level.  
1.3 This approach may be used for demonstrating compliance with in-process, validation, or lot-release specifications.  
1.4 The system of units for this practice is not specified.  
1.5 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.  
1.6 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.

  • Standard
    8 pages
    English language
    sale 15% off
  • Standard
    8 pages
    English language
    sale 15% off
  • Standard
    8 pages
    English language
    sale 15% off
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.

  • Standard
    5 pages
    English language
    sale 15% off
  • Standard
    5 pages
    English language
    sale 15% off
ASTM
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 ...

  • Standard
    10 pages
    English language
    sale 15% off
ASTM
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.

  • Standard
    23 pages
    English language
    sale 15% off