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The generalizability crisis

Published online by Cambridge University Press:  21 December 2020

Tal Yarkoni Open the ORCID record for Tal Yarkoni [Opens in a new window]
Tal Yarkoni*
Affiliation:
Department of Psychology, The University of Texas at Austin, Austin, TX78712-1043, USAtyarkoni@gmail.com
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Abstract

Most theories and hypotheses in psychology are verbal in nature, yet their evaluation overwhelmingly relies on inferential statistical procedures. The validity of the move from qualitative to quantitative analysis depends on the verbal and statistical expressions of a hypothesis being closely aligned – that is, that the two must refer to roughly the same set of hypothetical observations. Here, I argue that many applications of statistical inference in psychology fail to meet this basic condition. Focusing on the most widely used class of model in psychology – the linear mixed model – I explore the consequences of failing to statistically operationalize verbal hypotheses in a way that respects researchers' actual generalization intentions. I demonstrate that although the “random effect” formalism is used pervasively in psychology to model intersubject variability, few researchers accord the same treatment to other variables they clearly intend to generalize over (e.g., stimuli, tasks, or research sites). The under-specification of random effects imposes far stronger constraints on the generalizability of results than most researchers appreciate. Ignoring these constraints can dramatically inflate false-positive rates, and often leads researchers to draw sweeping verbal generalizations that lack a meaningful connection to the statistical quantities they are putatively based on. I argue that failure to take the alignment between verbal and statistical expressions seriously lies at the heart of many of psychology's ongoing problems (e.g., the replication crisis), and conclude with a discussion of several potential avenues for improvement.

Keywords

Generalizationinferencephilosophy of sciencepsychologyrandom effectsstatistics
Type
Target Article
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Behavioral and Brain Sciences , Volume 45 , 2022 , e1
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Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Acosta, A., Adams, R. B. Jr., Albohn, D. N., Allard, E. S., Beek, T., Benning, S. D., … Zwaan, R. A. (2016). Registered replication report: Strack, Martin, & Stepper (1988). Perspectives on Psychological Science, 11(6), 917–928.Google Scholar
Alogna, V. K., Attaya, M. K., Aucoin, P., Bahník, Š, Birch, S., Birt, A. R., … Zwaan, R. A. (2014). Registered replication report: Schooler and Engstler-Schooler (1990). Perspectives on Psychological Science, 9(5), 556–578.CrossRefGoogle Scholar
Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59(4), 390–412.CrossRefGoogle Scholar
Balota, D. A., Yap, M. J., Hutchison, K. A., & Cortese, M. J. (2012). Megastudies: What do millions (or so) of trials tell us about lexical processing? In Adelman, J. S. (Ed.), Visual word recognition volume 1: Models and methods, orthography and phonology (pp. 90–115). Psychology Press.Google Scholar
Baribault, B., Donkin, C., Little, D. R., Trueblood, J. S., Oravecz, Z., van Ravenzwaaij, D., … Vandekerckhove, J. (2018). Metastudies for robust tests of theory. Proceedings of the National Academy of Sciences of the United States of America, 115(11), 2607–2612.CrossRefGoogle ScholarPubMed
Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68(3), 255–278.CrossRefGoogle ScholarPubMed
Bates, D., Maechler, M., Bolker, B., Walker, S., Christensen, R. H. B., Singmann, H., … Krivitsky, P. N. (2014). Lme4: Linear mixed-effects models using eigen and S4. R Package Version, 1(7), 1–23.Google Scholar
Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A., Wagenmakers, E.-J., & Berk, R., … Johnson, V. E. (2018). Redefine statistical significance. Nature Human Behaviour, 2(1), 6.CrossRefGoogle ScholarPubMed
Bergelson, E., Bergmann, C., Byers-Heinlein, K., Cristia, A., Cusack, R., & Dyck, K., … (2017). Quantifying sources of variability in infancy research using the infant-directed speech preference.Google Scholar
Borsboom, D., Mellenbergh, G. J., & van Heerden, J. (2003). The theoretical status of latent variables. Psychological Review, 110(2), 203–219.CrossRefGoogle ScholarPubMed
Breiman, L. (2001). Statistical modeling: The two cultures. Statistical Science, 16(3), 199–215.CrossRefGoogle Scholar
Brennan, R. L. (1992). Generalizability theory. Educational Measurement: Issues and Practice, 11(4), 27–34.CrossRefGoogle Scholar
Brunswik, E. (1947). Systematic and representative design of psychological experiments. In Proceedings of the Berkeley symposium on mathematical statistics and probability (pp. 143–202).Google Scholar
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., … Riddell, A. (2017). Stan: A probabilistic programming language. Journal of Statistical Software, 76(1), 1–32.CrossRefGoogle Scholar
Chabris, C. F., Hebert, B. M., Benjamin, D. J., Beauchamp, J., Cesarini, D., van der Loos, M., … Laibson, D. (2012). Most reported genetic associations with general intelligence are probably false positives. Psychological Science, 23(11), 1314–1323.CrossRefGoogle ScholarPubMed
Cheung, I., Campbell, L., LeBel, E. P., Ackerman, R. A., Aykutoğlu, B., Bahník, Š, … Yong, J. C. (2016). Registered replication report: Study 1 from Finkel, Rusbult, Kumashiro, & Hannon (2002). Perspectives on Psychological Science, 11(5), 750–764.CrossRefGoogle Scholar
Clark, H. H. (1973). The language-as-fixed-effect fallacy: A critique of language statistics in psychological research. Journal of Verbal Learning and Verbal Behavior, 12(4), 335–359.CrossRefGoogle Scholar
Cohen, J. (2016). The earth is round (p < 0.05). In Harlow, L. L., Mulaik, S. A., & Steiger, J. H. (Eds.), What if there were no significance tests? (pp. 69–82). Routledge.Google Scholar
Coleman, E. B. (1964). Generalizing to a language population. Psychological Reports, 14(1), 219–226.CrossRefGoogle Scholar
Colhoun, H. M., McKeigue, P. M., & Davey Smith, G. (2003). Problems of reporting genetic associations with complex outcomes. Lancet (London, England), 361(9360), 865–872.CrossRefGoogle ScholarPubMed
Cornfield, J., & Tukey, J. W. (1956). Average values of mean squares in factorials. The Annals of Mathematical Statistics, 27(4), 907–949.CrossRefGoogle Scholar
Crabbe, J. C., Wahlsten, D., & Dudek, B. C. (1999). Genetics of mouse behavior: Interactions with laboratory environment. Science, 284(5420), 1670–1672.CrossRefGoogle ScholarPubMed
Crits-Christoph, P., & Mintz, J. (1991). Implications of therapist effects for the design and analysis of comparative studies of psychotherapies. Journal of Consulting and Clinical Psychology, 59(1), 20–26.CrossRefGoogle ScholarPubMed
Cronbach, L. J. (1975). Beyond the two disciplines of scientific psychology. American Psychologist, 30(2), 116.CrossRefGoogle Scholar
Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302.CrossRefGoogle ScholarPubMed
Cronbach, L. J., Rajaratnam, N., & Gleser, G. C. (1963). Theory of generalizability: A liberalization of reliability theory. The British Journal of Mathematical and Statistical Psychology, 16(2), 137–163.CrossRefGoogle Scholar
Draper, D. (1995). Assessment and propagation of model uncertainty. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 45–70.Google Scholar
Ebstein, R. P., Novick, O., Umansky, R., Priel, B., Osher, Y., Blaine, D., … Belmaker, R. H. (1996). Dopamine D4 receptor (D4DR) exon III polymorphism associated with the human personality trait of novelty seeking. Nature Genetics, 12(1), 78–80.CrossRefGoogle ScholarPubMed
Eerland, A. S., Magliano, A. M., Zwaan, J. P., Arnal, R. A., Aucoin, J. D., & Crocker, P. (2016). Registered replication report: Hart & Albarracín (2011). Perspectives on Psychological Science, 11(1), 158–171.CrossRefGoogle Scholar
Feynman, R. P. (1974). Cargo cult science. Engineering Sciences, 37(7), 10–13.Google Scholar
Francis, G. (2012). Publication bias and the failure of replication in experimental psychology. Psychonomic Bulletin & Review, 19(6), 975–991.CrossRefGoogle ScholarPubMed
Gelman, A. (2015). The connection between varying treatment effects and the crisis of unreplicable research: A Bayesian perspective. Journal of Management, 41(2), 632–643.CrossRefGoogle Scholar
Gelman, A. (2016). The problems with p-values are not just with p-values. The American Statistician, 70(supplemental material to the ASA statement on p-values and statistical significance), 10.Google Scholar
Gelman, A. (2018). The failure of null hypothesis significance testing when studying incremental changes, and what to do about it. Personality and Social Psychology Bulletin, 44(1), 16–23.CrossRefGoogle Scholar
Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.CrossRefGoogle Scholar
Gelman, A., & Loken, E. (2013). The garden of forking paths: Why multiple comparisons can be a problem, even when there is no “fishing expedition” or “p-hacking” and the research hypothesis. Downloaded January, 1–17.Google Scholar
Gelman, A., & Shalizi, C. R. (2013). Philosophy and the practice of Bayesian statistics. British Journal of Mathematical and Statistical Psychology, 66(1), 8–38.CrossRefGoogle ScholarPubMed
Gigerenzer, G. (2004). Mindless statistics. The Journal of Socio-Economics, 33(5), 587–606.CrossRefGoogle Scholar
Gigerenzer, G. (2017). A theory integration program. Decision, 4(3), 133.CrossRefGoogle Scholar
Gigerenzer, G., & Marewski, J. N. (2015). Surrogate science: The idol of a universal method for scientific inference. Journal of Management, 41(2), 421–440.CrossRefGoogle Scholar
Guion, R. M. (1980). On Trinitarian doctrines of validity. Professional Psychology, 11(3), 385–398.CrossRefGoogle Scholar
Hamilton, L. S., & Huth, A. G. (2018). The revolution will not be controlled: Natural stimuli in speech neuroscience. Language, Cognition and Neuroscience, 35(5), 573–582.CrossRefGoogle Scholar
Hofman, J. M., Sharma, A., & Watts, D. J. (2017). Prediction and explanation in social systems. Science, 355(6324), 486–488.CrossRefGoogle ScholarPubMed
Huth, A. G., de Heer, W. A., Griffiths, T. L., Theunissen, F. E., & Gallant, J. L. (2016). Natural speech reveals the semantic maps that tile human cerebral cortex. Nature, 532(7600), 453–458.CrossRefGoogle ScholarPubMed
Huth, A. G., Nishimoto, S., Vu, A. T., & Gallant, J. L. (2012). A continuous semantic space describes the representation of thousands of object and action categories across the human brain. Neuron, 76(6), 1210–1224.CrossRefGoogle ScholarPubMed
Ioannidis, J. (2008). Why most discovered true associations are inflated. Epidemiology (Cambridge, Mass.), 19(5), 640–648.CrossRefGoogle ScholarPubMed
Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine, 2(8), e124.CrossRefGoogle ScholarPubMed
John, L. K., Loewenstein, G., & Prelec, D. (2012). Measuring the prevalence of questionable research practices with incentives for truth telling. Psychological Science, 23(5), 524–532.CrossRefGoogle ScholarPubMed
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.CrossRefGoogle ScholarPubMed
Judd, C. M., Westfall, J., & Kenny, D. A. (2012). Treating stimuli as a random factor in social psychology: A new and comprehensive solution to a pervasive but largely ignored problem. Journal of Personality and Social Psychology, 103(1), 54–69.CrossRefGoogle ScholarPubMed
Keuleers, E., & Balota, D. A. (2015). Megastudies, crowdsourcing, and large datasets in psycholinguistics: An overview of recent developments. The Quarterly Journal of Experimental Psychology, 68(8), 1457–1468.CrossRefGoogle ScholarPubMed
Kruschke, J. K., & Liddell, T. M. (2017). The Bayesian new statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. Psychonomic Bulletin & Review, 25, 178–206.CrossRefGoogle Scholar
Kühberger, A., Fritz, A., & Scherndl, T. (2014). Publication bias in psychology: A diagnosis based on the correlation between effect size and sample size. PLoS ONE, 9(9), e105825.CrossRefGoogle ScholarPubMed
Lakens, D. (2017). Equivalence tests: A practical primer for t tests, correlations, and meta-analyses. Social Psychological and Personality Science, 8(4), 355–362.CrossRefGoogle Scholar
Lakens, D., Adolfi, F. G., Albers, C. J., Anvari, F., Apps, M. A. J., Argamon, S. E., … Zwaan, R. A. (2018). Justify your alpha. Nature Human Behaviour, 2(3), 168–171.CrossRefGoogle Scholar
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.CrossRefGoogle ScholarPubMed
Lesch, K. P., Bengel, D., Heils, A., Sabol, S. Z., Greenberg, B. D., Petri, S., … Murphy, D. L. (1996). Association of anxiety-related traits with a polymorphism in the serotonin transporter gene regulatory region. Science, 274(5292), 1527–1531.CrossRefGoogle ScholarPubMed
Lilienfeld, S. O. (2004). Taking theoretical risks in a world of directional predictions. Applied and Preventive Psychology, 11(1), 47–51.CrossRefGoogle Scholar
Lilienfeld, S. O. (2017). Psychology's replication crisis and the grant culture: Righting the ship. Perspectives on Psychological Science, 12(4), 660–664.CrossRefGoogle Scholar
Lykken, D. T. (1968). Statistical significance in psychological research. Psychological Bulletin, 70(3), 151–159.CrossRefGoogle ScholarPubMed
MacLeod, C. M. (1991). Half a century of research on the Stroop effect: An integrative review. Psychological Bulletin, 109(2), 163–203.CrossRefGoogle Scholar
Marewski, J. N., & Olsson, H. (2009). Beyond the null ritual: Formal modeling of psychological processes. Zeitschrift für Psychologie/Journal of Psychology, 217(1), 49–60.CrossRefGoogle Scholar
Matuschek, H., Kliegl, R., Vasishth, S., Baayen, H., & Bates, D. (2017). Balancing type I error and power in linear mixed models. Journal of Memory and Language, 94, 305–315.CrossRefGoogle Scholar
Mayo, D. G. (1991). Novel evidence and severe tests. Philosophy of Science, 58(4), 523–552.CrossRefGoogle Scholar
Mayo, D. G. (2018). Statistical inference as severe testing. Cambridge University Press.CrossRefGoogle Scholar
McShane, B. B., Gal, D., Gelman, A., Robert, C., & Tackett, J. L. (2019). Abandon statistical significance. The American Statistician, 73(Suppl. 1), 235–245.CrossRefGoogle Scholar
Meehl, P. (1997). The problem is epistemology, not statistics: Replace significance tests by confidence intervals and quantify accuracy of risky numerical predictions. In Harlow, L. L., Mulaik, S. A. & Steiger, J. H. (Eds.), What if there were no significance tests? (pp. 393–425). Erlbaum.Google Scholar
Meehl, P. E. (1967). Theory-testing in psychology and physics: A methodological paradox. Philosophy of Science, 34(2), 103–115.CrossRefGoogle Scholar
Meehl, P. E. (1978). Theoretical risks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progress of soft psychology. Journal of Consulting and Clinical Psychology, 46(4), 806.CrossRefGoogle Scholar
Meehl, P. E. (1986). What social scientists don't understand. In Fiske, D. W. & Shweder, R. A. (Eds.), Metatheory in social science: Pluralisms and subjectivities (pp. 315–338). University of Chicago Press.Google Scholar
Meehl, P. E. (1990a). Appraising and amending theories: The strategy of Lakatosian defense and two principles that warrant it. Psychological Inquiry, 1(2), 108–141.CrossRefGoogle Scholar
Meehl, P. E. (1990b). Why summaries of research on psychological theories are often uninterpretable. Psychological Reports, 66(1), 195–244.CrossRefGoogle Scholar
Meissner, C. A., & Brigham, J. C. (2001). A meta-analysis of the verbal overshadowing effect in face identification. Applied Cognitive Psychology, 15(6), 603–616.CrossRefGoogle Scholar
Meissner, C. A., & Memon, A. (2002). Verbal overshadowing: A special issue exploring theoretical and applied issues. Applied Cognitive Psychology, 16(8), 869–872.CrossRefGoogle Scholar
Moshontz, H., Campbell, L., Ebersole, C. R., IJzerman, H., Urry, H. L., Forscher, P. S., … Chartier, C. R. (2018). Psychological science accelerator: Advancing psychology through a distributed collaborative network. Advances in Methods and Practices in Psychological Science, 1(4), 501–515.CrossRefGoogle ScholarPubMed
Nagel, M., Jansen, P. R., Stringer, S., Watanabe, K., de Leeuw, C. A., Bryois, J., … Posthuma, D. (2018). Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways. Nature Genetics, 50(7), 920–927.CrossRefGoogle ScholarPubMed
O'Leary-Kelly, S. W., & Vokurka, R. J. (1998). The empirical assessment of construct validity. Journal of Operations Management, 16(4), 387–405.CrossRefGoogle Scholar
Pashler, H., & Wagenmakers, E.-J. (2012). Editors’ introduction to the special section on replicability in psychological science: A crisis of confidence? Perspectives on Psychological Science, 7(6), 528–530.CrossRefGoogle Scholar
Popper, K. (2014). Conjectures and refutations: The growth of scientific knowledge. Routledge.CrossRefGoogle Scholar
Reuss, H., Kiesel, A., & Kunde, W. (2015). Adjustments of response speed and accuracy to unconscious cues. Cognition, 134, 57–62.CrossRefGoogle ScholarPubMed
Roberts, S., & Pashler, H. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358.CrossRefGoogle Scholar
Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian T tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16(2), 225–237.CrossRefGoogle Scholar
Rozin, P. (2001). Social psychology and science: Some lessons from Solomon Asch. Personality and Social Psychology Review, 5(1), 2–14.CrossRefGoogle Scholar
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., … Fei-Fei, L. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211–252.CrossRefGoogle Scholar
Salvatier, J., Wiecki, T. V., & Fonnesbeck, C. (2016). Probabilistic programming in python using PyMC3. PeerJ Computer Science, 2, e55.CrossRefGoogle Scholar
Savage, J. E., Jansen, P. R., Stringer, S., Watanabe, K., Bryois, J., de Leeuw, C. A., … Posthuma, D. (2018). Genome-wide association metaanalysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nature Genetics, 50(7), 912–919.CrossRefGoogle Scholar
Schooler, J. W., & Engstler-Schooler, T. Y. (1990). Verbal overshadowing of visual memories: Some things are better left unsaid. Cognitive Psychology, 22(1), 36–71.CrossRefGoogle ScholarPubMed
Shavelson, R. J., & Webb, N. M. (1991). Generalizability theory: A primer. SAGE.Google Scholar
Shmueli, G. (2010). To explain or to predict? Statistical Science, 25(3), 289–310.CrossRefGoogle Scholar
Shrout, P. E., & Rodgers, J. L. (2018). Psychology, science, and knowledge construction: Broadening perspectives from the replication crisis. Annual Review of Psychology, 69, 487–510.CrossRefGoogle ScholarPubMed
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-Positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359–1366.CrossRefGoogle ScholarPubMed
Simons, D. J., Holcombe, A. O., & Spellman, B. A. (2014). An introduction to registered replication reports at perspectives on psychological science. Perspectives on Psychological Science, 9(5), 552–555.CrossRefGoogle ScholarPubMed
Simons, D. J., Shoda, Y., & Lindsay, D. S. (2017). Constraints on generality (COG): A proposed addition to all empirical papers. Perspectives on Psychological Science, 12(6), 1123–1128.CrossRefGoogle ScholarPubMed
Smaldino, P. E. (2017). Models are stupid, and we need more of them. In Vallacher, R. R., Read, S. J., & Nowak, A. (Eds.), Computational social psychology (pp. 311–331). Routledge.CrossRefGoogle Scholar
Smaldino, P. E., & McElreath, R. (2016). The natural selection of bad science. Royal Society Open Science, 3(9), 160384.CrossRefGoogle ScholarPubMed
Smedslund, J. (1991). The pseudoempirical in psychology and the case for psychologic. Psychological Inquiry, 2(4), 325–338.CrossRefGoogle Scholar
Spiers, H. J., & Maguire, E. A. (2007). Decoding human brain activity during real-world experiences. Trends in Cognitive Sciences, 11(8), 356–365.CrossRefGoogle ScholarPubMed
Steckler, A., McLeroy, K. R., Goodman, R. M., Bird, S. T., & McCormick, L. (1992). Toward integrating qualitative and quantitative methods: An introduction. Health Education Quarterly, 19(1), 1–8.CrossRefGoogle ScholarPubMed
Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18(6), 643.CrossRefGoogle Scholar
Sullivan, P. F. (2007). Spurious genetic associations. Biological Psychiatry, 61(10), 1121–1126.CrossRefGoogle ScholarPubMed
Tong, C. (2019). Statistical inference enables bad science; statistical thinking enables good science. The American Statistician, 73(Suppl. 1), 246–261.CrossRefGoogle Scholar
Trafimow, D. (2014). Editorial. Basic and Applied Social Psychology, 36(1), 1–2.CrossRefGoogle Scholar
Trafimow, D., & Marks, M. (2015). Editorial. Basic and Applied Social Psychology, 37(1), 1–2.CrossRefGoogle Scholar
Van Bavel, J. J., Mende-Siedlecki, P., Brady, W. J., & Reinero, D. A. (2016). Contextual sensitivity in scientific reproducibility. Proceedings of the National Academy of Sciences of the United States of America, 113(23), 6454–6459.CrossRefGoogle ScholarPubMed
Wagenmakers, E.-J. (2007). A practical solution to the pervasive problems of p values. Psychonomic Bulletin & Review, 14(5), 779–804.CrossRefGoogle Scholar
Wahlsten, D., Metten, P., Phillips, T. J., Boehm, S. L., Burkhart-Kasch, S., & Dorow, J., … (2003). Different data from different labs: Lessons from studies of gene–environment interaction. Journal of Neurobiology, 54(1), 283–311.CrossRefGoogle ScholarPubMed
Walker, H. A., & Cohen, B. P. (1985). Scope statements: Imperatives for evaluating theory. American Sociological Review, 50, 288–301.CrossRefGoogle Scholar
Westfall, J., Nichols, T. E., & Yarkoni, T. (2016). Fixing the stimulus-as-fixed-effect fallacy in task fMRI. Wellcome Open Research, 1, 23.CrossRefGoogle ScholarPubMed
Wolsiefer, K., Westfall, J., & Judd, C. M. (2017). Modeling stimulus variation in three common implicit attitude tasks. Behavior Research Methods, 49(4), 1193–1209.CrossRefGoogle ScholarPubMed
Woolston, C. (2015). Psychology journal bans P values. Nature News, 519(7541), 9.CrossRefGoogle Scholar
Wray, N. R., Ripke, S., Mattheisen, M., Trzaskowski, M., Byrne, E. M., & Abdellaoui, A., … Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. (2018). Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nature Genetics, 50(5), 668–681.CrossRefGoogle ScholarPubMed
Yarkoni, T. (2009). Big correlations in little studies: Inflated fMRI correlations reflect low statistical power-commentary on Vul et al. (2009). Perspectives on Psychological Science, 4(3), 294–298.CrossRefGoogle Scholar
Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100–1122.CrossRefGoogle ScholarPubMed