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?"
The horizontal axis represents the investment the organization makes. As investment increases, the organization spends more resources on improving the quality (remember, Noriaka was a quality guy at heart) or adding new capabilities.
The vertical dimension represents the satisfaction of the user, moving from an extreme negative of frustration to an extreme positive of delight. (Neutral satisfaction being neither frustrated nor delighted is in the middle of the axis.)
It’s against the backdrop of these two axes that we see how the Kano Model works. It shows us there are three forces at work, which we can use to predict our users’ satisfaction with the investment we make.