The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
When a good fit can be bad
Overview
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Registry
Cites
Statistics
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Claims the paper makes
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Self-check
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Citations
Cites
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
Model selection and multimodel inference: a practical information-theoretic approach.
Guthery, F. S., Burnham, K. P., & Anderson, D. R. (2003) · The Journal of Wildlife Management · doi:10.2307/3802723 · Cited by 41,495
Neural networks for pattern recognition
(1994) · Choice Reviews Online · doi:10.5860/choice.31-5500 · Cited by 18,538
Information Theory and an Extension of the Maximum Likelihood Principle
Akaike, H. (1998) · Springer Series in Statistics · doi:10.1007/978-1-4612-1694-0_15 · Cited by 17,781
An Introduction to Kolmogorov Complexity and Its Applications
Li, M., & Vitányi, P. (2019) · Texts in Computer Science · doi:10.1007/978-3-030-11298-1 · Cited by 3,681
Attention, similarity, and the identification-categorization relationship.
Nosofsky, R. M. (1986) · Journal of Experimental Psychology General · doi:10.1037//0096-3445.115.1.39 · Cited by 2,766
The psychometric function: I. Fitting, sampling, and goodness of fit
Wichmann, F. A., & Hill, N. J. (2001) · Perception & Psychophysics · doi:10.3758/bf03194544 · Cited by 2,358
Kass, R. E., & Raftery, A. E. (1995) · Journal of the American Statistical Association · doi:10.2307/2291091 · Cited by 2,312
How persuasive is a good fit? A comment on theory testing.
Roberts, S., & Pashler, H. (2000) · Psychological Review · doi:10.1037//0033-295x.107.2.358 · Cited by 875
Linhart, H., & Zucchini, W. (1986) · Cited by 869
Cited by
Rethinking some of the rethinking of partial least squares
Hair, J. F., Sarstedt, M., & Ringle, C. M. (2019) · European Journal of Marketing · doi:10.1108/ejm-10-2018-0665 · Cited by 1,839
Bayesian model selection for group studies
Stephan, K. E., Penny, W. D., Daunizeau, J., Moran, R. J., & Friston, K. J. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.03.025 · Cited by 1,423
Ten simple rules for dynamic causal modeling
Stephan, K. E., Penny, W. D., Moran, R. J., den Ouden, H. E. M., Daunizeau, J., & Friston, K. J. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.11.015 · Cited by 803
Sensitivity and specificity of information criteria
(2019) · doi:10.1093/bib/bbz016 · Cited by 718
An algorithm for identifying nonsystematic delay-discounting data.
Johnson, M. W., & Bickel, W. K. (2008) · Experimental and Clinical Psychopharmacology · doi:10.1037/1064-1297.16.3.264 · Cited by 555
Metadata
- Authors
- Pitt, M. A., & Myung, I. J.
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2002-10
- Volume
- 6
- Issue
- 10
- Pages
- 421-425
- ISSN
- 1364-6613 (ISSN-L)
- OpenAlex
- W2140386792
- Cited by
- 409
- References
- 22
- FWCI
- 2.90
- Type
- journal article
- Language
- en
- Open access
- closed
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: When a good fit can be bad. https://app.cochat.ai/papers/10.1016/s1364-6613(02)01964-2
- The paper
- Pitt, M. A., & Myung, I. J. (2002). When a good fit can be bad. Trends in Cognitive Sciences. https://doi.org/10.1016/s1364-6613(02)01964-2
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
