But this kind of manual test case derivation is error-prone and knowing these errors makes it possible to provide guidelines to reduce them.
Not knowing the error rate is bad enough, but some experts consistently testify under oath that their technique has an error rate of zero, an inherently preposterous claim.
Moreover, we investigate whether knowing the error rates influences behavior in the experiments.
On the contrary, knowing the error rates seems to have a negative effect on the heuristics used for solving the tasks.
The observation that performance in EXP-1S* is better than performance in EXP-1S suggests that the participants could not take advantage of knowing the error rates.
The problem with this technique is that it is difficult to establish a sensible criterion to evaluate the closeness of matched units without knowing estimation errors of the propensity scores.The present study introduces interval matching using bootstrap confidence intervals for accommodating estimation errors of propensity scores.
The study also gives the attenuation which a numerical model should provide in the reference situation in order to guarantee a minimum attenuation when knowing the errors in the spatial distribution of the primary field.
Because water distribution systems tend to grow over time with more recent pipe typically installed further from a population center, we were interested in knowing whether errors were geographically concentrated in a particular region.
The problem with this technique is that it is difficult to establish a sensible criterion to evaluate the closeness of matched units without knowing estimation errors of the propensity scores.
My unscientific method (read: guessing and rounding to the nearest thousand) surely left some room for error, and I know I'll sleep better at night knowing that that error is covered.
For example, in the existent PSM techniques, matching is done primarily based on the distance between point estimates of propensity scores, and thus, it is difficult to establish a meaningful criterion to evaluate the closeness of the matched units without knowing the estimation errors (or standard errors) of the estimated propensity scores.
