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Statistical reasoning is an art and so demands both mathematical knowledge and informed judgment. When it is mechanized, as with the institutionalized hybrid logic, it becomes ritual, not reasoning. Many experts have argued that it is not going to be easy to get researchers in psychology and other sociobiomedical sciences to drop this comforting crutch unless one offers an easy-to-use substitute. This chapter argues that this should be avoided — the substitution of one mechanistic dogma for another. At the very least, this chapter can serve as a tool in arguments with people who think they have to defend a ritualistic dogma instead of good statistical reasoning. Making and winning such arguments is indispensable to good science.

This chapter gives some examples of the importance of statistical and quantitative reasoning in public health surveillance, provides a framework for understanding the sort of data that can be compiled from an effective public health surveillance system, and identifies some useful sources of worldwide public health surveillance data. The framework portrays the relationships between the population and types of data collection in terms of opportunities for prevention, levels of intervention, and important health outcomes. Types of data are organized by time from earliest prevention opportunity: hazard surveillance, surveillance of health care interventions and public health activities, and surveillance of health outcomes. The category of hazard surveillance includes surveillance of both risk factors and preventive behaviors. Surveillance of health-care interventions and public-health activities consists of obtaining data on interventions directed to populations or individuals. Finally, surveillance of health outcomes includes collecting morbidity and mortality data.

Deborah Mayo (1996) has reinterpreted classic frequentist statistics into a much more general framework that she calls error statistics to indicate the continuing centrality and importance of error probabilities and error-probabilistic reasoning in testing hypotheses. Her generalization of statistical reasoning above and beyond any one statistical test provides a consistent and coherent approach to testing and assessing both quantitative and qualitative evidence and hence can be directly applied to whole-ecosystem experiments. This chapter argues that understanding the types of errors that replication controls allows for better design and interpretation of unreplicated and semi-replicated whole-ecosystem experiments. It begins by clarifying the meaning of three common concepts used in debates about what can and cannot be learned from whole-ecosystem manipulations: replication, BACI design, and pseudoreplication. It then rephrases Stuart Hurlbert's first error of concern and discusses replication as a check on stochastic events beyond natural variation.

Science adds to our understanding of ADHD by working out mechanisms of causation, symptom production, and intervention. So far, despite alternative hypotheses, the sciences have also stood by models that align with DSM-defined ADHD. Why? In part because—allowing for significant gaps in scientific knowledge—the data fit, by and large. But science’s commitment to certain reasoning patterns and methodologies also encourages adherence. For example, sticking with the same model makes it simpler to demonstrate data convergence across fields: researchers can be more certain that they are comparing apples to apples. Similarly, the common focus on difference (typically difference of “ADHD” from “non-ADHD”) meshes well with the DSM’s categorical portrayal of ADHD. These under-recognized effects of methodology, among other factors, slow down exploration of alternatives. Common methodologies and scientific goals also encourage dichotomization, reductionistic biological perspectives, and reification of ADHD—three aspects of the predominant view that contribute to intolerance.