Public Strong Inference Certain systematic methods of scientific thinking may produce much more rapid progress than others, Platt, 1964

[Snow Leopard]

Strong Inference
Certain systematic methods of scientific thinking may produce much more rapid progress than
others.

John R. Platt
Science, October 1964.
https://pubmed.ncbi.nlm.nih.gov/17739513/

http://pages.cs.wisc.edu/~markhill/science64_strong_inference.pdf

Scientists these days tend to keep up a polite fiction that all science is equal. Except for the work of the misguided opponent whose arguments we happen to be refuting at the time, we speak as though every scientist's field and methods of study are as good as every other scientist's, and perhaps a little better. This keeps us all cordial when it comes to recommending each other for government grants.

Personal comment: the more things change, the more things stay the same?

 

[Snow Leopard]

Another quote.

I think the evident effectiveness of the systematic use of strong inference suddenly gives us a yardstick for thinking about the effectiveness of scientific methods in general. Surveys, taxonomy, design of equipment, systematic measurements and tables, theoretical computations -- all have their proper and honored place, provided they are parts of a chain of precise induction of how nature works. Unfortunately, all too often they become ends in themselves, mere time-serving from the point of view of real scientific advance, a hypertrophied methodology that justifies itself as a lore of respectability.

We speak piously of taking measurements and making small studies that will "add another brick to the temple of science." Most such bricks just lie around the brickyard.

The great value of mathematical formulation is that when an experiment agrees with a calculation to five decimal places, a great many alternative hypotheses are pretty well excluded (though the Bohr theory and the Schrödinger theory both predict exactly the same Rydberg constant!) But when the fit is only to two decimal places, or maybe one, it man be no better than any rule-of-thumb extrapolation, and some other kind of qualitative exclusion might be more rigorous for testing the assumptions and more important to scientific understanding than quantitative fit. I know that this is like saying that the emperor has no clothes. Today we preach that science is not science unless it is quantitative. We substitute correlations for causal studies, and physical equations for organic reasoning. Measurements and equations are supposed to sharpen thinking, but, in my observation, they more often tend to make the thinking noncausal and fuzzy