There is a hidden cost to having a hypothesis. It arises from the relationship between night science and day science, the two very distinct modes of activity in which scientific ideas are generated and tested, respectively [1, 2]. With a hypothesis in hand, the impressive strengths of day science are unleashed, guiding us in designing tests, estimating parameters, and throwing out the hypothesis if it fails the tests. But when we analyze the results of an experiment, our mental focus on a specific hypothesis can prevent us from exploring other aspects of the data, effectively blinding us to new ideas.
Walking intrigues the deskbound. We romanticize it, but do we do it justice? Do we walk properly? Can one walk improperly and, if so, what happens when the walk is corrected?
This talk centered on Hamming's observations and research on the question "Why do so few scientists make significant contributions and so many are forgotten in the long run?"
Initial designs for sophisticated software applications are invariably complicated, even when developed by competent engineers. Truly good solutions emerge after iterative improvements or after redesigns that exploit new insights, and the most rewarding iterations are those that result in program simplifications.
Evolutions of this kind, however, are extremely rare in current software practice—they require time-consuming thought processes that are rarely rewarded. Instead, software inadequacies are typically corrected by quickly conceived additions that invariably result in the well-known bulk.