After 4 decades of severe criticism, the ritual of null hypothesis significance testing—mechanical dichotomous decisions around a sacred.05 criterion—still persists. This article reviews the problems with this practice, including its near-universal misinterpretation ofp as the probability that Ho is false, the misinterpretation that its complement is the probability of successful replication, and the mistaken assumption that if one rejects Ho one thereby affirms the theory that led to the test. Exploratory data analysis and the use of graphic methods, a steady improvement in and a movement toward standardization in measurement, an emphasis on estimating effect sizes using confidence intervals, and the informed use of available statistical methods is suggested. For generalization, psychologists must finally rely, as has been done in all the older sciences,

A literature review was conducted to understand the limitations of well-known statistical analysis techniques, particularly analysis of variance. The review is structured around six major points: (1) averaging across participants can be misleading; (2) strong predictions are preferable to weak predictions; (3) constructs and measures should be distinguished conceptually and empirically; (4) statistical signi ® cance and practical signi ® cance should be distinguished conceptually and empirically; (5) the null hypothesis is virtually never true; and (6) one experiment is always inconclusive. Based on these insights, a number of lesser-known and less-frequently used statistical analysis techniques were identi ® ed to address the limitations of more traditional techniques. In addition, a number of methodological conclusions about the conduct of human factors research are presented. 1.

In recent years the view has been advocated that the motor system is immune to visual size illusions. This is regarded as a consequence of a fundamental division of labor in the primate brain: vision for action versus vision for perception. We tested this claim for the Muller--Lyer illusion and the Parallel--Lines illusion. Both illusions clearly affected grasping. The effects on grasping were similar though not perfectly equal to the effects on perception. We present evidence that these small differences are due to problems in matching the perceptual and the motor tasks. We argue that grasping is an inherently unipolar measure because only one object can be grasped with the same hand at a time, while perceptual measures usually are bipolar because they require a comparison of two objects. We show that this difference is a central problem in matching the perceptual and the motor task. It has been claimed that grasping an object is not at all or hardly at all affected by visual illus...

'I should like to see anew conceptual apparatus of a logically and behaviourally straightforward kindbywhich to formulate, for scientific purposes, the sort of psychologica l information that is conveyed nowadays by idioms of propositional attitude.' W.V.O Quine (1978) ‘On our view, many of the illusions cannot be dispelled by a "few moments ' prompted reflection," or several months of college teaching; if dispelled, the illusions seem to return in full force the next time a similar situation comes along. Such illusions seem to be rooted very deeply in the human mind.’

Developmental psychopathology stands poised at the close of the 20th century on the horns of a major scientific dilemma. The essence of this dilemma lies in the contrast between its heuristically rich open system concepts on the one hand, and the closed system paradigm it adopted from mainstream psychology for investigating those models on the other. Many of the research methods, assessment strategies, and data analytic models of psychology’s paradigm are predicated on closed system assumptions and explanatory models. Thus, they are fundamentally inadequate for studying humans, who are unparalleled among open systems in their wide ranging capacities for equifinal and multifinal functioning. Developmental psychopathology faces two challenges in successfully negotiating the developmentalist’s dilemma. The first lies in recognizing how the current paradigm encourages research practices that are antithetical to developmental principles, yet continue to flourish. I argue that the developmentalist’s dilemma is sustained by long standing, mutually enabling weaknesses in the paradigm’s discovery methods and scientific standards. These interdependent weaknesses function like a distorted lens on the research process by variously sustaining the illusion of theoretical progress, obscuring the need for fundamental reforms, and both constraining and misguiding reform efforts. An understanding of how these influences arise and take their toll provides a foundation and rationale for engaging the second challenge. The essence of this challenge will be finding ways to resolve the developmentalist’s dilemma outside the constraints of the existing paradigm by developing indigenous research strategies, methods, and standards with fidelity to the complexity of developmental phenomena.

Many public and private genome-wide association studies that we have analyzed include flaws in design, with avoidable confounding appearing as a norm rather than the exception. Rather than recognizing flawed research design and addressing that, a category of quality-control statistical methods has arisen to treat only the symptoms. Reflecting more deeply, we examine elements of current genomic research in light of the traditional scientific method and find that hypotheses are often detached from data collection, experimental design, and causal theories. Association studies independent of causal theories, along with multiple testing errors, too often drive health care and public policy decisions. In an era of large-scale biological research, we ask questions about the role of statistical analyses in advancing coherent theories of diseases and their mechanisms. We advocate for reinterpretation of the scientific method in the context of large-scale data analysis opportunities and for renewed appreciation of falsifiable hypotheses, so that we can learn more from our best mistakes.