The "Tau" of Science - How to Measure, Study, and Integrate Quantitative and Qualitative Knowledge

Daniele Fanelli

No 67sak, MetaArXiv from Center for Open Science

Abstract: Scientists' ability to integrate diverse forms of evidence and evaluate how well they can explain and predict phenomena, in other words, $\textit{to know how much they know}$, struggles to keep pace with technological innovation. Central to the challenge of extracting knowledge from data is the need to develop a metric of knowledge itself. A candidate metric of knowledge, $K$, was recently proposed by the author. This essay further advances and integrates that proposal, by developing a methodology to measure its key variable, symbolized with the Greek letter $\tau$ ("tau"). It will be shown how a $\tau$ can represent the description of any phenomenon, any theory to explain it, and any methodology to study it, allowing the knowledge about that phenomenon to be measured with $K$. To illustrate potential applications, the essay calculates $\tau$ and $K$ values of: logical syllogisms and proofs, mathematical calculations, empirical quantitative knowledge, statistical model selection problems, including how to correct for "forking paths" and "P-hacking" biases, randomised controlled experiments, reproducibility and replicability, qualitative analyses via process tracing, and mixed quantitative and qualitative evidence. Whilst preliminary in many respects, these results suggest that $K$ theory offers a meaningful understanding of knowledge, which makes testable metascientific predictions, and which may be used to analyse and integrate qualitative and quantitative evidence to tackle complex problems.

Date: 2022-01-07
New Economics Papers: this item is included in nep-knm
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Persistent link: https://EconPapers.repec.org/RePEc:osf:metaar:67sak

DOI: 10.31219/osf.io/67sak

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