Distribution estimation by computer simulation

Probability distribution functions can be estimated by microcomputer simulation or by using mainframes. There is a tendency to substitute arithmetic power (bruite force) for analytic intelligence. Logically modeling a relationship and identifying the driving variables often yields more insight than voluminous computation; at least, experimental effort can be greatly reduced by careful pre-analysis. The size of sampling error inherent in the simulation process is not fully appreciated. Sample sizes required for satisfactory levels of precision are frequently larger than intuitively expected by the analyst with little statistical training. Many types of realistic simulations cannot be executed efficiently on desktop computers because of their relatively slow speed, particularly when the sampling is done in conjunction with spreadsheet software.