# ASTM G16-2019 pdf free download

ASTM G16-2019 pdf free download,Standard Guide for Applying Statistics to Analysis of Corrosion Data

1. Scope

1.1 This guide covers and presents briefly some generally accepted methods ofstatistical 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. 1.5 This international standard was developed in accor- dance with internationally recognized principles on standard- ization established in the Decision on Principles for the Development of International Standards, Guides and Recom- mendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.

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 investiga- tors 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 contrib- ute to the error ofmeasured 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 at least three points; 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 are required to construct a histogram, it is unusual to find data sets in corrosion work that lend themselves to this type of analysis.4.3 Normal Distribution—Many statistical techniques are based on the normal distribution. This distribution is bell- shaped and symmetrical. Use of analysis techniques developed for the normal distribution on data distributed in another manner can lead to grossly erroneous conclusions. Thus, before attempting data analysis, the data should either be verified as being scattered like a normal distribution, or a transformation should be used to obtain a data set which is approximately normally distributed. Transformed data may be analyzed sta- tistically and the results transformed back to give the desired results, although the process of transforming the data back can create problems in terms ofnot having symmetrical confidence intervals. 4.4 Normal Probability Paper—If the histogram is not confirmatory in terms of the shape of the distribution, the data may be examined further to see if it is normally distributed by constructing a normal probability plot as described as follows (1). 3 4.4.1 It is easiest to construct a normal probability plot if normal probability paper is available. This paper has one linear axis, and one axis which is arranged to reflect the shape of the cumulative area under the normal distribution. In practice, the “probability” axis has 0.5 or 50 % at the center, a number approaching 0 percent at one end, and a number approaching 1.0 or 100 % at the other end. The marks are spaced far apart in the center and close together at the ends. A normal probability plot may be constructed as follows with normal probability paper. N OTE 1—Data that plot approximately on a straight line on the probability plot may be considered to be normally distributed. Deviations from a normal distribution may be recognized by the presence of deviations from a straight line, usually most noticeable at the extreme ends of the data.