No scientific conclusion follows automatically from a statistically non-significant result, yet people routinely use non-significant results to guide conclusions about the status of theories (or the effectiveness of practices). To know whether a non-significant result counts against a theory, or if it just indicates data insensitivity, researchers must use one of: power, intervals (such as confidence or credibility intervals), or else an indicator of the relative evidence for one theory over another, such as a Bayes factor. I argue Bayes factors (...) allow theory to be linked to data in a way that overcomes the weaknesses of the other approaches. Specifically, Bayes factors use the data themselves to determine their sensitivity in distinguishing theories (unlike power), and they make use of those aspects of a theory’s predictions that are often easiest to specify (unlike power and intervals, which require specifying the minimal interesting value in order to address theory). Bayes factors provide a coherent approach to determining whether non-significant results support a null hypothesis over a theory, or whether the data are just insensitive. They allow accepting and rejecting the null hypothesis to be put on an equal footing. Concrete examples are provided to indicate the range of application of a simple online Bayes calculator, which reveal both the strengths and weaknesses of Bayes factors. (shrink)

This article described three heuristics that are employed in making judgements under uncertainty: representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and adjustment from an anchor, which is usually employed in numerical prediction when a relevant value (...) is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty. (shrink)

: To what extent do moral judgments depend on conscious reasoning from explicitly understood principles? We address this question by investigating one particular moral principle, the principle of the double effect. Using web-based technology, we collected a large data set on individuals’ responses to a series of moral dilemmas, asking when harm to innocent others is permissible. Each moral dilemma presented a choice between action and inaction, both resulting in lives saved and lives lost. Results showed that: patterns of moral (...) judgments were consistent with the principle of double effect and showed little variation across differences in gender, age, educational level, ethnicity, religion or national affiliation and a majority of subjects failed to provide justifications that could account for their judgments. These results indicate that the principle of the double effect may be operative in our moral judgments but not open to conscious introspection. We discuss these results in light of current psychological theories of moral cognition, emphasizing the need to consider the unconscious appraisal system that mentally represents the causal and intentional properties of human action. (shrink)

What Chow calls NHSTP is an inconsistent hybrid of Fisherian and Neyman-Pearsonian ideas. In psychology it has been practiced like ritualistic handwashing and sustained by wishful thinking about its utility. Chow argues that NHSTP is an important tool for ruling out chance as an explanation for data. I disagree. This ritual discourages theory development by providing researchers with no incentive to specify hypotheses.

To what extent do moral judgments depend on conscious reasoning from explicitly understood principles? We address this question by investigating one particular moral principle, the principle of the double effect. Using web-based technology, we collected a large data set on individuals' responses to a series of moral dilemmas, asking when harm to innocent others is permissible. Each moral dilemma presented a choice between action and inaction, both resulting in lives saved and lives lost. Results showed that: (1) patterns of moral (...) judgments were consistent with the principle of double effect and showed little variation across differences in gender, age, educational level, ethnicity, religion or national affiliation (within the limited range of our sample population) and (2) a majority of subjects failed to provide justifications that could account for their judgments. These results indicate that the principle of the double effect may be operative in our moral judgments but not open to conscious introspection. We discuss these results in light of current psychological theories of moral cognition, emphasizing the need to consider the unconscious appraisal system that mentally represents the causal and intentional properties of human action. (shrink)

We created paired moral dilemmas with minimal contrasts in wording, a research strategy that has been advocated as a way to empirically establish principles operative in a domain‐specific moral psychology. However, the candidate “principles” we tested were not derived from work in moral philosophy, but rather from work in the areas of consumer choice and risk perception. Participants were paradoxically less likely to choose an action that sacrifices one life to save others when they were asked to provide more reasons (...) for doing so (Experiment 1), and their willingness to sacrifice lives depended not only on how many lives would be saved, but on the number of lives at risk (Experiment 2). The latter effect was also found in a within‐subjects design (Experiment 3). These findings suggest caution in the use of artificial dilemmas as a key testbed for revealing principled bases for moral judgment. (shrink)