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Stepwise model fitting and statistical inference: turning noise into signal pollution.
1Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, D-04103 Leipzig, Germany. roger_mundry@eva.mpg.de
The American Naturalist
|December 4, 2008
Summary
Statistical inference using stepwise model selection inflates Type I error rates, leading to false positives in ecological and behavioral research. Biologists should avoid these methods due to their unreliability.
Area of Science:
- Ecology
- Evolutionary Biology
- Behavioral Science
Background:
- Stepwise model selection is frequently used in ecological, evolutionary, and behavioral research.
- This method has fundamental limitations in identifying the optimal model and suffers from a multiple-testing problem.
Purpose of the Study:
- To demonstrate the inflated Type I error rates associated with stepwise procedures.
- To compare the performance of stepwise regression against simultaneous model entry.
Main Methods:
- A simulation study using artificial data sets with uncorrelated variables.
- Comparison of stepwise regression with a model including all predictor variables simultaneously.
Main Results:
- Stepwise procedures resulted in greatly inflated Type I error rates.
- Significance tests based on stepwise methods are unreliable for hypothesis testing.
Conclusions:
- Stepwise model selection procedures lead to a high probability of false discoveries.
- Biologists are strongly advised against using stepwise methods in statistical inference.
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