ASTM G16:19 pdf download

ASTM G16:19 pdf download.Standard Guide for Applying Statistics to Analysis of Corrosion Data.
4.10 Mistakes—Mistakes either in carrying out an experiment or in calculations are not a characteristic of the population and can preclude statistical treatment of data or lead to erroneous conclusions if included in the analysis. Sometimes mistakes can be identified by statistical methods by recognizing that the probability of obtaining a particular result is very low.
4.11 Outlying Observations—See Practice El 78 for procedures for dealing with outlying observations.
5. Central Measures
5.1 It is accepted practice to employ several independent (replicate) measurements of any experimental quantity to improve the estimate of precision and to reduce the variance of the average value. If it is assumed that the processes operating to create error in the measurement are random in nature and are as likely to overestimate the true unknown value as to underestimate it, then the average value is the best estimate of the unknown value in question. The average value is usually indicated by placing a bar over the symbol representing the measured variable.
Nom 3—In this standard, the term “mean” is reserved to describe a central measure of a population, while average refers to a sample.
5.2 If processes operate to exaggerate the magnitude of the error either in overestimating or underestimating the correct measurement, then the median value is usually a better estimate.
5.3 If the processes operating to create error affect both the probability and magnitude of the error, then other approaches must be employed to lind the best estimation procedure. A qualified statistician should be consulted in this case.
5.4 In corrosion testing. it is generally observed that average values are useful in characterizing corrosion rates. In cases of penetration from pitting and cracking, failure is often defined as the first through penetration and in these cases, average penetration rates or times are of little value. Extreme value analysis has been used in these cases, see Guide G46.
5.5 When the average value is calculated and reported as the only result in experiments when several replicate runs were made, information on the scatter of data is lost.
6. Variability Measures
6.1 Several measures of distribution variability are available which can be useful in estimating confidence intervals and making predictions from the observed data. In the case of normal distribution, a number of procedures are available and can he handled with computer programs. These measures include the following: variance, standard deviation, and coefficient of variation. The range is a useful non-parametric estimate of variability and can be used with both normal and other distributions.
6.2 Variance—Variance, 2, may be estimated for an experimental data set of n observations by computing the sample estimated vanance, S, assuming all observations arc subject to the same errors.