Scientific Realism and the No Miracles Argument

The No Miracles Argument (NMA) is perhaps the most prominent argument in the debate about scientific realism. It contends that the truth of our best scientific theories is the only hypothesis that does not make the astonishing predictive and explanatory success of science a mystery. However, the argument has been criticized from a Bayesian point of view as committing the base rate fallacy. We provide two Bayesian models (one related to the individual-theory-based NMA and one related to the frequency-based NMA) that respond to that objection. The first model takes into account the observed stability of mature scientific theories, the second the success frequency of theories within a scientific discipline. We conclude that the NMA can be used to defend the realist thesis and that its validity is a highly context-sensitive matter.

What the argument from evil should, but cannot, be

Michael Tooley has developed a sophisticated evidential version of the argument from evil that aims to circumvent sceptical theist responses. Evidential arguments from evil depend on the plausibility of inductive inferences from premises about our inability to see morally sufficient reasons for God to permit evils to conclusions about there being no morally sufficient reasons for God to permit evils. Tooley's defence of this inductive step depends on the idea that the existence of unknown rightmaking properties is no more likely, a priori, than the existence of unknown wrongmaking properties. I argue that Tooley's argument begs the question against the theist, and, in doing so, commits an analogue of the base rate fallacy. I conclude with some reflections on what a successful argument from evil would have to establish.

Statistical Evidence in Health and Risk Messaging

Persuasive messages use statistical evidence in order to convince an audience to accept a conclusion. Statistical evidence represents a compilation of experiences structured and collected in a manner that permits expression in mathematical form. Research demonstrates that the use of statistical evidence increases the persuasiveness of a message, and a message that uses both statistical and narrative evidence generates the greatest persuasiveness. Statistical evidence can take the form of summarizing the collective opinion of experts on a topic or an expression of the collective set of experiences. The challenge becomes gaining acceptance of statistical expressions of experience versus what is perceived as the narrative or lived experience of the single person. Statistical evidence is often presented using a mathematical expression to indicate the size or force of the evidence. The accumulation of statistical evidence often involves the use of meta-analysis to reduce Type I (false positive) and Type II (false negative) error. The use of evidence is strategic and can target specific elements of belief by understanding the structure of beliefs and the connectivity among elements. The use of the Subjective Probability Model provides a means to capitalize on the use of evidence by changing probabilities in beliefs to increase the effectiveness of a message campaign. Statistical evidence, however, may be ineffective under circumstances referred to as the “base-rate fallacy.” The base-rate fallacy occurs when the presentation of statistical information is accepted, but examples are used that contradict the base-rate. The impact of the use of the example is to create a shift in the belief in the typicality of the example, despite knowledge of the base-rate. Fear appeals provide a particularly useful and important application of statistical evidence in the pursuit of public health campaigns. The tenets of the Extended Parallel Processing Model indicate that message effectiveness relies on a combination of: (a) perceived severity of the threat, (b) perceived vulnerability to the threat, (c) perceived efficacy of the solution, and (d) perceived personal efficacy of the solution. Each element is largely impacted by the application and use of statistical information to make claims. The use of statistics generally outlines the argument and supports the conclusion offered in support of a conclusion offered to the message recipient. Statistical evidence when used in a message often offers data or information that becomes the justification for a conclusion. A large part of a message becomes gaining acceptance of information by an audience, then explaining (reasoning) to the audience how those facts support a conclusion, often involving some type of recommendation for behavior. Understanding statistical evidence requires understanding how the material functions within the context of the belief system of the individual.