Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Simulation Tests of Lot Size Programming

This paper presents the results of some digital computer simulation tests of a procedure for the economic planning of lot sizes, work force, and inventories. A dynamic, deterministic, linear programming model was used to obtain approximate solutions to the actual problem which is both dynamic and stochastic. The tests were made with data taken from an actual factory. An alternate procedure, based upon single-item inventory control, was also tested; its results were compared with those obtained from the linear programming model. On the basis of these tests, this linear programming method appears to offer a promising method for the practical economic planning of production activities.