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Why Hypothesis Testers Should Spend Less Time Testing Hypotheses
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Abstract
For almost half a century, Paul Meehl educated psychologists about how the mindless use of null-hypothesis significance tests made research on theories in the social sciences basically uninterpretable. In response to the replication crisis, reforms in psychology have focused on formalizing procedures for testing hypotheses. These reforms were necessary and influential. However, as an unexpected consequence, psychological scientists have begun to realize that they may not be ready to test hypotheses. Forcing researchers to prematurely test hypotheses before they have established a sound "derivation chain" between test and theory is counterproductive. Instead, various nonconfirmatory research activities should be used to obtain the inputs necessary to make hypothesis tests informative. Before testing hypotheses, researchers should spend more time forming concepts, developing valid measures, establishing the causal relationships between concepts and the functional form of those relationships, and identifying boundary conditions and auxiliary assumptions. Providing these inputs should be recognized and incentivized as a crucial goal in itself. In this article, we discuss how shifting the focus to nonconfirmatory research can tie together many loose ends of psychology's reform movement and help us to develop strong, testable theories, as Paul Meehl urged.
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“Due to this lack of theory and robust data, we as AI researchers might simply not be ready to test specific hypotheses for certain contexts-a conclusion that is not unheard of in other research fields, such as psychology (Scheel et al., 2021) and human-robot interaction (Leichtmann et al., 2022).”
From Effects of Explainable Artificial Intelligence on trust and human behavior in a high-risk decision task (2023) · cites this paper as Scheel et al., 2021
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“Scheel et al. (2021) suggested that, before testing hypotheses confirmatorily, prior steps such as exploratory experimentation can help to discover relationships between variables and to explore boundaries on the basis of which more concrete theoretical assumptions can be made.”
From Effects of Explainable Artificial Intelligence on trust and human behavior in a high-risk decision task (2023) · cites this paper as Scheel et al., 2021
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“Thus, it becomes evident that more specific theorizing enables more precise operationalizations and more adequate empirical testing (Scheel et al., 2021).”
From Do Salient Social Norms Moderate Mortality Salience Effects? A (Challenging) Meta-Analysis of Terror Management Studies (2023) · cites this paper as Scheel et al., 2021
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“Even though the studies have confirmed the significance and influence of BPN Satisfaction for HCI research, the inconsistent use of measures is problematic in terms of comparability between studies and their replicability.”
From Development and Validation of a Basic Psychological Needs Scale for Technology Use (2024) · cites this paper as Scheel et al. (2021)
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“If a study fails to apply a valid measure, theoretical inferences cannot be made as the findings could equally measure random noise or a related but nevertheless distinct construct (Scheel et al., 2021).”
From Development and Validation of a Basic Psychological Needs Scale for Technology Use (2024) · cites this paper as Scheel et al., 2021
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“…for understanding such differences (Autzen, 2021).1 Replication may also be hindered by inappropriate theoretical accounts of observed phenomena (Eronen & Bringmann, 2021; Levenstein et al., 2023) and by confusing mere descriptions of the phenomena with their explanations (Scheel et al., 2020).”
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Wildman, A., & Ramsey, R. (2021) · Quarterly Journal of Experimental Psychology · doi:10.1177/17470218211047447 · Cited by 4
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“Scholars have emphasised the importance of careful descriptive work prior to explanation, prediction, and intervention (Rozin, 2009; Scheel et al., 2021).”
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“More broadly, the field of psychological sciences has recognized issues with conventional, reductionistic approaches to hypothesis testing, stemming from validity and conceptual challenges related to the subjective and contextual nature of the studied phenomenon (Scheel et al., 2021).”
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“Given the extent of inconsistencies identified, we encourage researchers to engage in less aimless hypothesis testing (Scheel et al., 2020) and accept the need for more meta-work to establish clearer foundations and address bigger questions.”
Dyslexia Research and Replicability: Should We Be Worried?
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“Fuzzy conceptualization and the diversity in operationalization can further lead to variability in hypothesis formulation (Scheel et al., 2021).”
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Rhee, L., Ross, M. Q., Le, H., Mount, J., Chang, Y.-J., & Bayer, J. (2026) · Behaviour and Information Technology · doi:10.1080/0144929x.2026.2619109 · Cited by 1
“However, recent perspectives have reasserted how a data-driven approach helps to answer the ‘what’ questions and thereby assists in building concepts, creating measures, and identifying phenomena (Scheel et al. 2021) – including for social behaviour (Harari et al. 2017).”
Calignano, G., Perugini, A., Nucci, M., Finos, L., & Pastore, M. (2026) · Psychonomic Bulletin & Review · doi:10.3758/s13423-026-02946-z · Cited by 1
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“…number between e-commerce, and customer satisfaction is 0.568, which means that it has a positive relationship, while from the results of hypothesis testing (Scheel et al. , 2021), it is obtained count of 5.50 and table of 1.681, this means that count> table, then Ha is accepted and Ho is rejected.”
ResultInfluentialDimensions of predictive success
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“Our assessment will focus on the extent to which each manuscript contributes to the accumulation of knowledge (Scheel et al., 2021), rather than on whether it presents an entirely new and sensational theoretical claim about language processing.”
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Motivations of a Practical Orientation
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Grossmann, M. (2021) · Oxford University Press eBooks · doi:10.1093/oso/9780197518977.003.0004
Multiple Levels of Analysis and Time Scales
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Popularization and Consilience
Grossmann, M. (2021) · Oxford University Press eBooks · doi:10.1093/oso/9780197518977.003.0010
Grossmann, M. (2021) · Oxford University Press eBooks · doi:10.1093/oso/9780197518977.003.0005
Metadata
- Authors
- Scheel, A. M., Tiokhin, L., Isager, P. M., & Lakens, D.
- Affiliations
- Eindhoven University of Technology (NL)
- Corresponding author
- Scheel, Anne M.
- Venue
- Perspectives on Psychological Science
- Publisher
- SAGE Publications
- Published
- 2020-12-16
- Volume
- 16
- Issue
- 4
- Pages
- 744-755
- ISSN
- 1745-6916 (ISSN-L)
- 1745-6924
- PMC
- PMC8273364
- OpenAlex
- W3113132966
- Cited by
- 382
- References
- 95
- Citations by year
- 2026: 32 · 2025: 48 · 2024: 82 · 2023: 69 · 2022: 80 · 2021: 59 · 2020: 12
- FWCI
- 143.27
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Perspectives on Psychological Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- TU/e Research Portal (publishedVersion, cc-by)
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- PsyArXiv (OSF Preprints) (submittedVersion)
- TU/e Research Portal (submittedVersion, cc-by)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (VIDI grant 452-17-013)
- Topics
- Social Sciences › Psychology › General Psychology › Academic and Historical Perspectives in Psychology
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Psychology Research and Bibliometrics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- null hypothesis significance testing, hypothesis testing, replication crisis, Paul Meehl, theory testing, measurement validity, auxiliary hypotheses, boundary conditions
- MeSH terms
- Humans, Psychological Theory, Psychology, Causality
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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