Inference and generality in ecology: Current problems and an experimental solution

Two major points are considered: 1) the growth of ecology is constrained and the scientific credibility of ecologists is reduced, because too many generalizations are accepted on insufficient evidence, and 2) there are experimental methods for addressing this problem. I illustrate problems that arise from theories that, although valid in limited domains, have been excessively generalized, and I examine methods for decreasing the disparity in populations considered between statistical inferences and scientific generalizations. The use of random factors in experimental designs helps to address the domain in which generalizations are valid, because the statistical population encompassed by random factors is much larger than that addressed typically by fixed factors. The use of random factors is considered in particular in mixed-model ANOVAs, because the basic techniques are well described and familiar to most ecologists. Mixed-model designs provide a strong experimental framework and address many of the problems with other methods for assessing the generality of ecological theories. No single experimental design will be sufficient to define the broader generalities that ecologists seek, but these designs should be included more often in research programs.