Merritt, David (2022) The scientific method from a philosophical perspective.

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Abstract

A methodology of science must satisfy two requirements: (i) It must be ampliative: the theories which it generates must make statements that go far beyond any data or observations that may have motivated those theories in the first place. (ii) It must be epistemically probative: it must somehow provide a warrant for believing that the theories so produced are correct, or at least partially correct, even if they can never be fully confirmed. These two requirements pull in opposite directions, and attempts to specify the “scientific method” often focus on one to the exclusion of the other. On a few points there now exists something approaching a consensus. (i) Scientific hypotheses — including, particularly, statements about unobserved or unobservable entities or mechanisms — remain conjectural, no matter how frequently predictions based on those hypotheses are found to coincide with data. (ii) A good (best?) indicator of a theory’s verisimilitude is its ability to successfully predict phenomena which it was not specifically designed to predict. I discuss these ideas with particular reference to cosmological theories.

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Item Type:	Published Article or Volume
Creators:	Creators Email ORCID Merritt, David david.r.merritt@gmail.com 0000-0002-9339-1551
Additional Information:	Text of invited talk at "The present and future of astronomy", 14 to 18 February, 2022
Keywords:	Cosmology Dark matter Scientific method
Subjects:	General Issues > Computer Simulation General Issues > Confirmation/Induction Specific Sciences > Physics > Cosmology General Issues > Models and Idealization
Depositing User:	David Merritt
Date Deposited:	13 Mar 2022 05:51
Last Modified:	13 Mar 2022 05:51
Item ID:	20329
Publisher:	Zenodo
Official URL:	https://zenodo.org/record/6336021#.Yii8gFNOlKO
DOI or Unique Handle:	10.5281/zenodo.6336021
Subjects:	General Issues > Computer Simulation General Issues > Confirmation/Induction Specific Sciences > Physics > Cosmology General Issues > Models and Idealization
Date:	8 March 2022
URI:	https://philsci-archive.pitt.edu/id/eprint/20329

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