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A Introduction

“Scientific reasoning requires a certain discipline and we should try to teach this discipline,” so said Richard Feynman,1 though he noted elsewhere that it actu­ally wasn't taught in any course that he knew about.

Apparently, according to my survey (Chapter 9), it mostly still isn't.

You might find “discipline” a bit off-putting, all grim self-denial and fla­vorless meals; however, improving your scientific thinking doesn't have to be aversive. Scientific thinking is orderly, not regimented or formulaic; it is cer­tainly not uncreative or Feynman, an extremely creative scientist, wouldn't have recommended it. The goal is to train your cognitive abilities so that you can learn and practice science productively, even enjoyably. True, it's not always easy to interpret data or use the hypothesis, but you already know that science is a challenging career. In any case, mastering any complex skill—basketball, piano, or scientific thinking—can be fun and stimulating while it increases your capabilities.

And it's not as if stilted engagement in disciplined thinking is mandatory every minute that you're doing science—spontaneous, unconscious mind-wandering is as important as self-abnegation—and, in any case, you have the logic of the hypothesis to guide you. In this chapter, I want to offer a few suggestions for acquiring the skill of thinking with the hypothesis. The first part outlines the exercises of finding and diagramming hypotheses in published work and your own. The second part suggests ways of thinking about your own thinking that may help you improve its quality.

You won't find these suggestions in conventional science textbooks. Rather, to shake things up, I've collected tips and principles that are not specifically sci­entific and that may seem unfamiliar, even odd, in the context of the Scientific Method. The hope is that if you come at the subject from a novel point of view, you'll see the discipline that Feynman is talking about in a new light.

14.

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Source: Alger Bradley E.. Defense of the Scientific Hypothesis: From Reproducibility Crisis to Big Data. Oxford University Press,2020. — 449 p.. 2020

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