Parkinson's Law states that "work expands to fill the time available for its completion." It was first articulated by C. Northcote Parkinson in the book Parkinson's Law: The Pursuit of Progress, (London, John Murray, 1958) based on extensive experience in the British Civil Service. The scientific observations which contributed to the law's development included noting that as Britain's overseas empire declined in importance, the number of employees at the Colonial Office increased.

I propose the GenIQ-Parkinson's Law that states "The GenIQ Model© expands to fill the time available for model completion – guaranteeing the best model for the allotted time.” The GenIQ modeling process is evolutionary; thus, it theoretically never stops, similar to human evolution. Unlike with humankind, there is a stopping rule (addition to various empirical ones) for GenIQ model development: “time is up.” The required GenIQ Model for a given amount of time is the “best” model among all models via other methods for a given amount of time. GenIQ can deliver the best model for the time available, as a direct consequence of its evolutionary paradigm and unique optimization of the decile analysis. This is an unparallel feature of the GenIQ Model that alternative methods can not offer. Statistical techniques like the logistic and ordinary least-squares regression models require time-consuming data preparation that sets the data analyst's actual start-time for model building after days of data preparation. GenIQ requires no data preparation, only 1] to insure there are no impossible or improbable values (e.g., age of 120 years, or a boy named Sue, respectively), and 2] to audit missing and zero values. The GenIQ-Parkinson's Laws is appropriate for situations when there is limited amount of time to build a model, because without a time restriction there is an equal playing field for all modeling techniques.

For a free-n-easy discussion of The GenIQ Model click here. 

For an eye-opening preview of the 9-step modeling process of GenIQ, click here