Practical Methodological Reform Needs Good Theory

Gervais, Will M.

doi:10.31234/osf.io/jcs6e

Cited by 10 publications

(15 citation statements)

References 44 publications

(81 reference statements)

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“…Models necessarily simplify and hence ignore many of the nuanced details of the real world, but this is a feature rather than a bug. Much has been written on the importance of formal models in the human sciences and related fields (Haldane, 1964;Levins, 1966;Wimsatt, 1987;Varian, 1997;Bedau, 1999;Schank, 2001;Turchin, 2003;Bryson et al, 2007;Epstein, 2008;Weisberg, 2012;Nowak et al, 2013;Gunawardena, 2014;Servedio et al, 2014;Morecroft, 2015;Smaldino et al, 2015;Eberlen et al, 2017;Smaldino, 2017Smaldino, , 2019aPage, 2018;Jolly & Chang, 2019;Muthukrishna & Henrich, 2019;Gervais, 2020;Guest & Martin, 2020;Robinaugh et al, 2020;van Rooij & Baggio, 2020), and I will not repeat those arguments here.…”

Section: Introductionmentioning

confidence: 99%

How to Translate a Verbal Theory Into a Formal Model

Smaldino

2020

Social Psychology

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Section: Introductionmentioning

confidence: 99%

How to Translate a Verbal Theory Into a Formal Model

Smaldino

2020

Social Psychology

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“…To examine the suitability of reproducibility as a demarcation criterion, a precise definition of reproducibility of results is necessary. While many definitions have been offered for replication and results reproducibility (see [44], for a partial list), most are informal and not sufficiently precise or general for our purposes 3 . In this paper, we use our own definitions based on first principles to facilitate the derivation of our theoretical results.…”

Section: Reproducibility Rate Is a Parameter Of The Population Of Stumentioning

confidence: 99%

“…Testing hypotheses with no theory to motivate them is a fishing expedition regardless of methodological rigor. See [44,[75][76][77][78][79][80][81][82][83][84] for discussions on scientific theory.…”

Section: Valid Conditional Inference Is Well Establishedmentioning

confidence: 99%

The case for formal methodology in scientific reform

et al. 2021

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Current attempts at methodological reform in sciences come in response to an overall lack of rigor in methodological and scientific practices in experimental sciences. However, most methodological reform attempts suffer from similar mistakes and over-generalizations to the ones they aim to address. We argue that this can be attributed in part to lack of formalism and first principles. Considering the costs of allowing false claims to become canonized, we argue for formal statistical rigor and scientific nuance in methodological reform. To attain this rigor and nuance, we propose a five-step formal approach for solving methodological problems. To illustrate the use and benefits of such formalism, we present a formal statistical analysis of three popular claims in the metascientific literature: (i) that reproducibility is the cornerstone of science; (ii) that data must not be used twice in any analysis; and (iii) that exploratory projects imply poor statistical practice. We show how our formal approach can inform and shape debates about such methodological claims.

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“…To examine the suitability of reproducibility as a demarcation criterion, a precise definition of reproducibility of results is necessary. While many definitions have been offered for replication and results reproducibility (see Gervais, 2020 , for a partial list), most are informal and not sufficiently precise or general for our purposes. 3 .…”

Section: Claim 1: Reproducibility Is the Cornerstone Of Or A Demarcamentioning

confidence: 99%

“… 8 Testing hypotheses with no theory to motivate them is a fishing expedition regardless of methodological rigor. See Gervais ( 2020 ); Guest and Martin ( 2020 ); Fried ( 2020 ); MacEachern and Van Zandt ( 2019 ); Muthukrishna and Henrich ( 2019 ); Oberauer and Lewandowsky ( 2019 ); Szollosi and Donkin ( 2019 ); Szollosi et al . ( 2019 ); van Rooij ( 2019 ); van Rooij and Baggio ( 2020a ) and van Rooij and Baggio ( 2020b ) for discussions on scientific theory.…”

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confidence: 99%

The case for formal methodology in scientific reform

Devezer

Navarro

Vandekerckhove

et al. 2020

Preprint

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Current attempts at methodological reform in sciences come in response to an overall 9 lack of rigor in methodological and scientific practices in experimental sciences. However, some of 10 these reform attempts suffer from the same mistakes and over-generalizations they purport to 11 address. Considering the costs of allowing false claims to become canonized, we argue for more 12 rigor and nuance in methodological reform. By way of example, we present a formal analysis of 13 three common claims in the metascientific literature: (a) that reproducibility is the cornerstone of 14 science; (b) that data must not be used twice in any analysis; and (c) that exploratory projects are 15 characterized by poor statistical practice. We show that none of these three claims are correct in 16 general and we explore when they do and do not hold. 17 18 42 1 of 28 Manuscript submitted.an opportunity: reformers are in an opportune position to take criticism and self-correct before 43 allowing false claims to be canonized as methodological facts (Nissen et al., 2016). 44 In this paper we advocate for the necessity of statistically rigorous and scientifically nuanced 45 arguments to make proper methodological claims in the reform literature. Toward this aim, we 46 evaluate three examples of methodological claims that have been advanced and well-accepted (as 47 implied by the large number of citations) in the reform literature: 48 1. Reproducibility is the cornerstone of, or a demarcation criterion for, science. 49 2. Using data more than once invalidates statistical inference. 50 3. Exploratory research uses "wonky" statistics. 51 Each of these claims suffers from some of the problems outlined earlier and as a result, has 52 contributed to methodological half-truths (or untruths). We evaluate each claim using statistical 53 theory against a broad philosophical and scientific background. 54 While we focus on these three claims, we believe our call for rigor and nuance can reach 55 further with the following emphasis: Statistics is a formal science whose methodological claims 56 follow from probability calculus. Methodological claims are either proved mathematically or by 57 simulation before being advanced for the use of scientists. Most valid methodological advances 58 are incremental, and they rarely ever provide simple prescriptions to complex inference problems. 59 Norms issued on the basis of bold claims about new methods might be quickly adopted by empirical 60 scientists as heuristics and might alter scientific practices. However, advancing such reforms in the 61 absence of formal proofs is sacrificing rigor for boldness and can lead to unforeseeable scientific 62 consequences. We believe that hasty revolution may hold science back more than it helps move 63 it forward. We hope that our approach may facilitate scientific progress that stands on firm 64 ground-supported by theory or evidence. 65 Claim 1: Reproducibility is the cornerstone of, or a demarcation criterion 66 for, science. 67 A common asser...

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Practical Methodological Reform Needs Good Theory

Cited by 10 publications

References 44 publications

How to Translate a Verbal Theory Into a Formal Model

How to Translate a Verbal Theory Into a Formal Model

The case for formal methodology in scientific reform

The case for formal methodology in scientific reform

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