The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Stepwise Model Fitting and Statistical Inference: Turning Noise into Signal Pollution
Open accessAbstract
Statistical inference based on stepwise model selection is applied regularly in ecological, evolutionary, and behavioral research. In addition to fundamental shortcomings with regard to finding the "best" model, stepwise procedures are known to suffer from a multiple-testing problem, yet the method is still widely used. As an illustration of this problem, we present results of a simulation study of artificial data sets of uncorrelated variables, with two to 10 predictor variables and one dependent variable. We then compared results from stepwise regression with a regression model in which all predictor variables were entered simultaneously. These analyses clearly demonstrate that significance tests based on stepwise procedures lead to greatly inflated Type I error rates (i.e., the probability of erroneously rejecting a true null hypothesis). By using a simple simulation design, our study amplifies previous warnings about using stepwise procedures, and we follow others in recommending that biologists refrain from applying these methods.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Tabachnick, B. G., & Fidell, L. S. (1983) · Cited by 79,866
Baltosser, W. H., & Zar, J. H. (1996) · Ecology · doi:10.2307/2265725 · Cited by 35,166
Discovering Statistics Using SPSS
Field, A. P., & Miles, J. N. V. (2000) · Medical Entomology and Zoology · Cited by 28,265
Biometry: The Principles and Practice of Statistics in Biological Research
Sokal, R. R., & Rohlf, F. J. (1969) · Cited by 20,963
Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences
Cohen, P., Cohen, P., West, S. G., & Aiken, L. S. (2014) · Psychology Press eBooks · doi:10.4324/9781410606266 · Cited by 20,737
Experimental Design and Data Analysis for Biologists
Quinn, G. P., & Keough, M. J. (2002) · doi:10.1017/cbo9780511806384 · Cited by 10,674 · cited 4×
“Multiple regression requires a large number of cases compared to the number of predictor variables (Tabachnick and Fidell 2001; Quinn and Keough 2002; Field 2005).”
BackgroundMethodInfluentialWitten, I. H. (2008) · Cited by 9,143
Biostatistical Analysis (2nd ed.).
Breen, T. J., & Zar, J. H. (1986) · Journal of the American Statistical Association · doi:10.2307/2289268 · Cited by 5,781
Model Selection and Multimodel Inference
(2003) · Technometrics · doi:10.1198/tech.2003.s146 · Cited by 5,449
Model selection in ecology and evolution
Johnson, J. B., & Omland, K. S. (2004) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2003.10.013 · Cited by 3,838
Cited by
Burnham, K. P., Anderson, D. R., & Huyvaert, K. P. (2011) · Behavioral Ecology and Sociobiology · doi:10.1007/s00265-010-1029-6 · Cited by 4,104
Forstmeier, W., & Schielzeth, H. (2011) · Behavioral Ecology and Sociobiology · doi:10.1007/s00265-010-1038-5 · Cited by 1,120
Detecting and avoiding likely false‐positive findings – a practical guide
Forstmeier, W., Wagenmakers, E., & Parker, T. H. (2017) · Biological Reviews · doi:10.1111/brv.12315 · Cited by 436
Estimating and controlling for spatial structure in the study of ecological communities
Peres‐Neto, P. R., & Legendre, P. (2010) · Global Ecology and Biogeography · doi:10.1111/j.1466-8238.2009.00506.x · Cited by 443
Tank, D. C., Eastman, J. M., Pennell, M. W., Soltis, P. S., Soltis, D. E., Hinchliff, C. E., Brown, J. W., Sessa, E. B., & Harmon, L. J. (2015) · New Phytologist · doi:10.1111/nph.13491 · Cited by 364
Metadata
- Authors
- Mundry, R., & Nunn, C. L.
- Venue
- The American Naturalist
- Publisher
- University of Chicago Press
- Published
- 2009-01
- Volume
- 173
- Issue
- 1
- Pages
- 119-123
- ISSN
- 0003-0147 (ISSN-L)
- 1537-5323
- OpenAlex
- W2167122694
- Cited by
- 440
- References
- 28
- FWCI
- 19.25
- Type
- journal article
- Language
- en
- Open access
- green
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Stepwise Model Fitting and Statistical Inference: Turning Noise into Signal Pollution. https://app.cochat.ai/papers/10.1086/593303
- The paper
- Mundry, R., & Nunn, C. L. (2009). Stepwise Model Fitting and Statistical Inference: Turning Noise into Signal Pollution. The American Naturalist. https://doi.org/10.1086/593303
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.