Wise use of statistical tools in ecological field studies

The currently dominating hypothetico-deductive research paradigm for ecology has statistical hypothesis testing as a basic element. Classic statistical hypothesis testing does, however, present the ecologist with two fundamental dilemmas when field data are to be analyzed: (1) that the statistically motivated demand for a random and representative sample and the ecologically motivated demand for representation of variation in the study area cannot be fully met at the same time; and (2) that the statistically motivated demand for independence of errors calls for sampling distances that exceed the scales of relevant pattern-generating processes, so that samples with statistically desirable properties will be ecologically irrelevant. Reasons for these dilemmas are explained by consideration of the classic statistical Neyman-Pearson test procedure, properties of ecological variables, properties of sampling designs, interactions between properties of the ecological variables and properties of sampling designs, and specific assumptions of the statistical methods. Analytic solutions to problems underlying the dilemmas are briefly reviewed. I conclude that several important research objectives cannot be approached without subjective elements in sampling designs. I argue that a research strategy entirely based on rigorous statistical testing of hypotheses is insufficient for field ecological data and that inductive and deductive approaches are complementary in the process of building ecological knowledge. I recommend that great care is taken when statistical tests are applied to ecological field data. Use of less formal modelling approaches is recommended for cases when formal testing is not strictly needed. Sets of recommendations, Guidelines for wise use of statistical tools, are proposed both for testing and for modelling. Important elements of wise-use guidelines are parallel use of methods that preferably belong to different methodologies, selection of methods with few and less rigorous assumptions, conservative interpretation of results, and abandonment of definitive decisions based a predefined significance level.