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STATISTICAL ANALYSIS OF MATERIAL DATA PART II: ON ESTIMATION AND TESTING

Introduction In the previous (first) article of this series, random variables (R.V.), distributions and parameters were overviewed and the problem of outliers was briefly discussed. Our objective was to provide practicing engineers with a more thorough understanding of the philosophy behind the statistical procedures they need to apply in their materials work. [1,2]. In this second article we pursue further this objective by discussing problems related with sampling, estimation and testing. We have seen how every random process (or R.V.) has two or more outputs that follow a distinctive pattern (its distribution). And we have seen how such a distribution can be uniquely specified by a set of fixed values or parameters. Once these two elements are known, we can answer all pertinent questions regarding the random process and thus take the necessary decisions to control, forecast or effect its course. Unfortunately, in almost every case the R.V. distribution and its associated parameters are unknown. Then, the best that we can do is to observe the process (i.e. sample) and use these sample observations to reconstruct both the distribution and the parameters that generated them (estimation) or to confirm or reject some educated guess that we have previously formed, about these distribution and parameters (hypothesis testing).