ASTM G16-13

Standard Guide for Applying Statistics to Analysis of Corrosion Data

SIGNIFICANCE AND USE
3.1 Corrosion test results often show more scatter than many other types of tests because of a variety of factors, including the fact that minor impurities often play a decisive role in controlling corrosion rates. Statistical analysis can be very helpful in allowing investigators to interpret such results, especially in determining when test results differ from one another significantly. This can be a difficult task when a variety of materials are under test, but statistical methods provide a rational approach to this problem.  
3.2 Modern data reduction programs in combination with computers have allowed sophisticated statistical analyses on data sets with relative ease. This capability permits investigators to determine if associations exist between many variables and, if so, to develop quantitative expressions relating the variables.  
3.3 Statistical evaluation is a necessary step in the analysis of results from any procedure which provides quantitative information. This analysis allows confidence intervals to be estimated from the measured results.
SCOPE
1.1 This guide covers and presents briefly some generally accepted methods of statistical analyses which are useful in the interpretation of corrosion test results.  
1.2 This guide does not cover detailed calculations and methods, but rather covers a range of approaches which have found application in corrosion testing.  
1.3 Only those statistical methods that have found wide acceptance in corrosion testing have been considered in this guide.  
1.4 The values stated in SI units are to be regarded as standard. No other units of measurement are included in this standard.

General Information

• Status: Historical
• Publication Date: 30-Nov-2013
• ICS: 07.020 - Mathematics
• ICS: 77.060 - Corrosion of metals
• Technical Committee: G01 - Corrosion of Metals
• Drafting Committee: G01.05 - Laboratory Corrosion Tests

Relations

• Replaces: ASTM G16-95(2010) - Standard Guide for Applying Statistics to Analysis of Corrosion Data
• Effective Date: 01-Dec-2013
• Replaced By: ASTM G16-13(2019) - Standard Guide for Applying Statistics to Analysis of Corrosion Data
• Effective Date: 15-Feb-2019
• Referred By: ASTM F3044-20 - Standard Test Method for Evaluating the Potential for Galvanic Corrosion for Medical Implants
• Effective Date: 01-Dec-2013
• Referred By: ASTM G208-12(2020) - Standard Practice for Evaluating and Qualifying Oilfield and Refinery Corrosion Inhibitors Using Jet Impingement Apparatus
• Effective Date: 01-Dec-2013
• Referred By: ASTM G71-81(2019) - Standard Guide for Conducting and Evaluating Galvanic Corrosion Tests in Electrolytes
• Effective Date: 01-Dec-2013
• Referred By: ASTM G82-98(2021)e1 - Standard Guide for Development and Use of a Galvanic Series for Predicting Galvanic Corrosion Performance
• Effective Date: 01-Dec-2013
• Referred By: ASTM G116-99(2020)e1 - Standard Practice for Conducting Wire-on-Bolt Test for Atmospheric Galvanic Corrosion
• Effective Date: 01-Dec-2013
• Referred By: ASTM G170-06(2020)e1 - Standard Guide for Evaluating and Qualifying Oilfield and Refinery Corrosion Inhibitors in the Laboratory
• Effective Date: 01-Dec-2013
• Referred By: ASTM G123-00(2022)e1 - Standard Test Method for Evaluating Stress-Corrosion Cracking of Stainless Alloys with Different Nickel Content in Boiling Acidified Sodium Chloride Solution
• Effective Date: 01-Dec-2013
• Referred By: ASTM G185-06(2020)e1 - Standard Practice for Evaluating and Qualifying Oil Field and Refinery Corrosion Inhibitors Using the Rotating Cylinder Electrode
• Effective Date: 01-Dec-2013
• Referred By: ASTM G67-18 - Standard Test Method for Determining the Susceptibility to Intergranular Corrosion of 5XXX Series Aluminum Alloys by Mass Loss After Exposure to Nitric Acid (NAMLT Test)
• Effective Date: 01-Dec-2013
• Referred By: ASTM F3268-18a - Standard Guide for <emph type="bdit">in vitro</emph> Degradation Testing of Absorbable Metals
• Effective Date: 01-Dec-2013
• Referred By: ASTM D4778-15 - Standard Test Method for Determination of Corrosion and Fouling Tendency of Cooling Water Under Heat Transfer Conditions
• Effective Date: 01-Dec-2013
• Referred By: ASTM G1-03(2017)e1 - Standard Practice for Preparing, Cleaning, and Evaluating Corrosion Test Specimens
• Effective Date: 01-Dec-2013
• Referred By: ASTM G91-11(2018) - Standard Practice for Monitoring Atmospheric SO<inf>2</inf> Deposition Rate for Atmospheric Corrosivity Evaluation
• Effective Date: 01-Dec-2013

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: G16 − 13
Standard Guide for
1
Applying Statistics to Analysis of Corrosion Data
ThisstandardisissuedunderthefixeddesignationG16;thenumberimmediatelyfollowingthedesignationindicatestheyearoforiginal
adoptionor,inthecaseofrevision,theyearoflastrevision.Anumberinparenthesesindicatestheyearoflastreapproval.Asuperscript
epsilon (´) indicates an editorial change since the last revision or reapproval.
1. Scope of materials are under test, but statistical methods provide a
rational approach to this problem.
1.1 This guide covers and presents briefly some generally
acceptedmethodsofstatisticalanalyseswhichareusefulinthe 3.2 Modern data reduction programs in combination with
interpretation of corrosion test results. computers have allowed sophisticated statistical analyses on
data sets with relative ease. This capability permits investiga-
1.2 This guide does not cover detailed calculations and
tors to determine if associations exist between many variables
methods, but rather covers a range of approaches which have
and, if so, to develop quantitative expressions relating the
found application in corrosion testing.
variables.
1.3 Only those statistical methods that have found wide
3.3 Statistical evaluation is a necessary step in the analysis
acceptance in corrosion testing have been considered in this
of results from any procedure which provides quantitative
guide.
information. This analysis allows confidence intervals to be
1.4 The values stated in SI units are to be regarded as
estimated from the measured results.
standard. No other units of measurement are included in this
4. Errors
standard.
4.1 Distributions—In the measurement of values associated
2. Referenced Documents
withthecorrosionofmetals,avarietyoffactorsacttoproduce
2
2.1 ASTM Standards:
measured values that deviate from expected values for the
E178Practice for Dealing With Outlying Observations
conditions that are present. Usually the factors which contrib-
E691Practice for Conducting an Interlaboratory Study to
utetotheerrorofmeasuredvaluesactinamoreorlessrandom
Determine the Precision of a Test Method
way so that the average of several values approximates the
G46Guide for Examination and Evaluation of Pitting Cor-
expected value better than a single measurement. The pattern
rosion
in which data are scattered is called its distribution, and a
IEEE/ASTM SI 10American National Standard for Use of
variety of distributions are seen in corrosion work.
theInternationalSystemofUnits(SI):TheModernMetric
4.2 Histograms—A bar graph called a histogram may be
System
used to display the scatter of the data. A histogram is
constructed by dividing the range of data values into equal
3. Significance and Use
intervals on the abscissa axis and then placing a bar over each
3.1 Corrosion test results often show more scatter than
interval of a height equal to the number of data points within
many other types of tests because of a variety of factors,
thatinterval.Thenumberofintervalsshouldbefewenoughso
including the fact that minor impurities often play a decisive
that almost all intervals contain at least three points; however,
role in controlling corrosion rates. Statistical analysis can be
there should be a sufficient number of intervals to facilitate
very helpful in allowing investigators to interpret such results,
visualization of the shape and symmetry of the bar heights.
especially in determining when test results differ from one
Twenty intervals are usually recommended for a histogram.
anothersignificantly.Thiscanbeadifficulttaskwhenavariety
Because so many points are required to construct a histogram,
it is unusual to find data sets in corrosion work that lend
themselves to this type of analysis.
1
This guide is under the jurisdiction ofASTM Committee G01 on Corrosion of
Metals and is the direct responsibility of Subcommittee G01.05 on Laboratory
4.3 Normal Distribution—Many statistical techniques are
Corrosion Tests.
based on the normal distribution. This distribution is bell-
Current edition approved Dec. 1, 2013. Published December 2013. Originally
shapedandsymmetrical.Useofanalysistechniquesdeveloped
approved in 1971. Last previous edition approved in 2010 as G16–95 (2010). DOI:
10.1520/G0016-13.
for the normal distribution on data distributed in another
2
For referenced ASTM standards, visit the ASTM website, www.astm.org, or
mannercanleadtogrosslyerroneousconclusions.Thus,before
contact ASTM Customer Service at [e-mail]. For Annual Book of ASTM
attempting data analysis, the data should either be verified as
Standards volume information, refer to the standard’s Document Summary page on
the ASTM website. being scattered like a normal distribution, or a transformation
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
1

...

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: G16 − 95 (Reapproved 2010) G16 − 13
Standard Guide for
1
Applying Statistics to Analysis of Corrosion Data
This standard is issued under the fixed designation G16; 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 guide covers and presents briefly some generally accepted methods of statistical analyses which are useful in the
interpretation of corrosion test results.
1.2 This guide does not cover detailed calculations and methods, but rather covers a range of approaches which have found
application in corrosion testing.
1.3 Only those statistical methods that have found wide acceptance in corrosion testing have been considered in this guide.
1.4 The values stated in SI units are to be regarded as standard. No other units of measurement are included in this standard.
2. Referenced Documents
2
2.1 ASTM Standards:
E178 Practice for Dealing With Outlying Observations
E691 Practice for Conducting an Interlaboratory Study to Determine the Precision of a Test Method
G46 Guide for Examination and Evaluation of Pitting Corrosion
IEEE/ASTM SI 10 American National Standard for Use of the International System of Units (SI): The Modern Metric System
3. Significance and Use
3.1 Corrosion test results often show more scatter than many other types of tests because of a variety of factors, including the
fact that minor impurities often play a decisive role in controlling corrosion rates. Statistical analysis can be very helpful in
allowing investigators to interpret such results, especially in determining when test results differ from one another significantly.
This can be a difficult task when a variety of materials are under test, but statistical methods provide a rational approach to this
problem.
3.2 Modern data reduction programs in combination with computers have allowed sophisticated statistical analyses on data sets
with relative ease. This capability permits investigators to determine if associations exist between many variables and, if so, to
develop quantitative expressions relating the variables.
3.3 Statistical evaluation is a necessary step in the analysis of results from any procedure which provides quantitative
information. This analysis allows confidence intervals to be estimated from the measured results.
4. Errors
4.1 Distributions—In the measurement of values associated with the corrosion of metals, a variety of factors act to produce
measured values that deviate from expected values for the conditions that are present. Usually the factors which contribute to the
error of measured values act in a more or less random way so that the average of several values approximates the expected value
better than a single measurement. The pattern in which data are scattered is called its distribution, and a variety of distributions
are seen in corrosion work.
4.2 Histograms—A bar graph called a histogram may be used to display the scatter of the data. A histogram is constructed by
dividing the range of data values into equal intervals on the abscissa axis and then placing a bar over each interval of a height equal
to the number of data points within that interval. The number of intervals should be few enough so that almost all intervals contain
1
This guide is under the jurisdiction of ASTM Committee G01 on Corrosion of Metals and is the direct responsibility of Subcommittee G01.05 on Laboratory Corrosion
Tests.
Current edition approved Feb. 1, 2010Dec. 1, 2013. Published March 2010December 2013. Originally approved in 1971. Last previous edition approved in 20042010 as
G16–95(2004).G16–95 (2010). DOI: 10.1520/G0016-95R10.10.1520/G0016-13.
2
For referenced ASTM standards, visit the ASTM website, www.astm.org, or contact ASTM Customer Service at [e-mail]. For Annual Book of ASTM Standards
volume information, refer to the standard’s Document Summary page on the ASTM website.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
1

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G16 − 13
at least three points, howeverpoints; however, there should be a sufficient number of intervals to facilitate visualization of the shape
and symmetry of the bar heights. Twenty intervals are usually recommended for a histogram. Because so many points a
...