Sampling Strategies for Destructive Tests
Keywords:Monte Carlo simulation, statistical methods, cost efficiency, concomitant variables
AbstractTwo strategies are considered whereby the full sample need not be destroyed to obtain estimated of low-order percentiles. The properties are examined by Monte Carlo methods. One strategy, based on a proof-loading concept, appears to have practical value, particularly when used in conjunction with a non-destructively-determined concomitant variable. Even when the concomitant variable has a fairly low correlation with the property of interest, the number of samples destroyed is appreciably reduced. This number is, however, a random variable. The second strategy, which again uses a concomitant variable, fixes the number destroyed, but in other respects is less satisfactory; for reliable results the number destroyed will be uncomfortably high unless the variables are very highly correlated. The use of a subjective ranking rather than a measured concomitant variable, to reduce the number of specimens destroyed, is considered. It is seen that, under some circumstances, subjective ranking, or even allocation into ordered groups, will be cost-efficient.
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