Abstract
Most of the claims we make, nowhere more so than in the empirical sciences, outrun the information enlisted to support them. Such claims are never more than probable/likely. Intuitively, even obviously, some claims are more probable/likely than others. Everyone agrees that scientific claims in particular are probable/likely to the extent that they are confirmed by experimental evidence. But there is very little agreement about what “confirmation by empirical evidence” involves or how it is to be measured. A central thesis of this monograph is that a source of this disagreement is the near-universal tendency to conflate the two different concepts—“confirmation” and “evidence”—used to formulate the essence of the methodology. There is no doubt that the words signifying them are used interchangeably. But as we will go on to argue, failure to make the distinction leads to muddled thinking in philosophy, statistics, and the empirical sciences themselves. Two examples, one having to do with the testing of traditional psychotherapeutic hypotheses, the other with determining the empirical superiority of the wave or particle theories of light, make it clear how data can confirm a hypothesis or theory, yet fail, in context, to provide evidence for it.
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Notes
- 1.
Section II, #20.
- 2.
The likelihood ratio is one among a number of evidence functions. We will later list the reasons for its choice.
- 3.
See Hacking (1975), for a lucid account of the history.
- 4.
Perhaps it can be explained in terms of the fact that philosophical claims are traditionally taken as true or false, to be established or refuted on the basis of deductive arguments. Logic and set theory are well suited to appraise their validity. In science, on the other hand, there is a much greater emphasis on quantitative models which are all strictly false, but whose adequacy is best assessed probabilistically.
- 5.
Hume understands “By probability, that evidence, which is still attended with uncertainty. ‘Tis this last species of reasoning I propose to examine” (1888/1739–1740, p. 124). It is significant that in this paragraph from A Treatise of Human Nature, Hume attributes “degrees” to evidence, associates it with “reasoning” or inference, and (implicitly) distinguishes it from “experience” (by which we understand “data”). In all three respects, our discussion follows his and breaks with what has come to be the conventional wisdom.
- 6.
One can distinguish between statistical and quantum uncertainty, but not isolate them. A certain amount of statistical uncertainty, on occasion not insignificant, will be due to quantum uncertainty. Butterfly-like amplifications translate some quantum uncertainty into statistical uncertainty. The behavior of animals responding to chemoreceptors is an example. Some chemoreceptors are so exquisitely sensitive that they will respond to single molecules. Whether or not a particular molecule comes into contact with a receptor depends on the quantum uncertainty of Brownian motion.
- 7.
The distinction between inferential and substantive uncertainty, the one having to do with the kinds of conclusions we humans are capable of drawing, the other with the course of natural phenomena, is not sharp. Statistical distributions are natural phenomena, but they often give the arguments in which they figure as premises an inductive character.
- 8.
There is little point in multiplying examples from the standard literature. Suffice it to note that advocates of “reliabilism,” perhaps the currently most popular “naturalized” epistemological theory, assimilate evidence and justification without apology. See Alvin Goldman’s seminal “A Causal Theory of Knowing” (Goldman 1967); he equates “a highly warranted inductive inference” with one which gives an epistemic agent “adequate evidence” for his belief. The most recent comprehensive summary of theories of confirmation with which we are familiar, the article “Confirmation” in the Stanford Encyclopedia of Philosophy (Crupi 2013/2014) simply assumes that evidence and confirmation are inter-definable regardless of the confirmation theory at issue, which is to say that no attempt is made by any of the theories discussed to distinguish “data” from “evidence”. For a more popular on-line assimilation, see the helpful article entitled “Evidence” in the Internet Encyclopedia of Philosophy (DiFate 2007): “In the philosophy of science, evidence is taken to be what confirms or refutes scientific theories” (p. 2) and again “evidence is that which justifies a person’s belief” (p. 7), although this latter claim is not simply assumed but argued for. It is worth adding, as a note to those who have told us on occasion that the discussion of evidence has advanced well beyond our characterization of it, that this relatively recent summary of the current situation focuses on exactly those positions and people that we do here.
- 9.
Not simply the inference at stake when we infer the description of and thus predict future events, but identify and select models to describe natural processes, estimate and select the values of parameters in the descriptive models, and assess the consistency of the evidence with all three. See, for example, (Cox 2006).
- 10.
- 11.
There are many good features of Bovens and Hartmann’s highly original application of both probability and Bayesian-network theories to solving philosophical problems (Bovens and Hartmann 2004). Our approach differs from theirs, however, in three respects. First, unlike their “engineering approach,” ours is a foundational investigation into issues concerning “evidence” and “confirmation”. Second, and again unlike theirs, ours is not simply an exercise in “Bayesian epistemology”. Third, like almost all writers on confirmation theory, they fall victim to the conflation of evidence and confirmation (see especially their chapter on confirmation).
- 12.
Or in denying that “evidence” and “confirmation” has each been endowed with a variety of different meanings. It is entirely typical of the literature, however, that in a foundational article, “Confirmation and Relevance” (Salmon 1975), Wesley Salmon first notes the ambiguities and then proceeds to focus on “investigating the conclusions that can be drawn from the knowledge that this or that evidence confirms this or that hypothesis”. Even, and perhaps especially, the very best philosophers working in the area cannot resist conflating the two concepts.
- 13.
At least this is the story we have long learned to tell. It is questioned, to put it mildly, by Oliver (1999).
- 14.
Rosenzweig (1936).
- 15.
See Luborsky et al., (2002) for an especially rigorous examination of 17 meta-analyses of the relevant data. What follows in the above paragraph draws on this examination. The article includes an extensive bibliography.
- 16.
Luborsky et al., claim that “there is much evidence already for their mostly good level of efficacy”. As noted, many, perhaps most, people talk this way. But it should be clear that the data do not provide evidence that any one of them is particularly efficacious.
- 17.
Those tempted to draw the conclusion that psychotherapies are “pseudo-sciences” need to be reminded of the fact, given wide publicity by the former head of Sloan-Kettering Cancer Institute, Lewis Thomas, that the body cures most of the diseases by which it is attacked, without any medical intervention. See Thomas (1974).
- 18.
Julian Reiss, to provide still another example, runs what we call “evidence” together with what we call “confirmation” in the usual way. He writes, “[a] good theory of evidence should be a theory of both support and warrant”. As he goes on to explain, “[s]upport pertains to the gathering of facts, warrant to the making up of one’s mind. Gathering of facts and making up one’s mind are different processes…[But] we cannot have warrant for a hypothesis without having support” (Reiss 2015, pp. 34–35). On his view, one cannot have “justification” without “evidence,” the conflation that we are attempting to undo. Along the same lines, he conflates “gathering facts” with “having evidence,” when the two activities are to be rather sharply distinguished.
- 19.
Although the discussion in this monograph is self-contained, readers who would like a more general introduction to some of the notation and basic concepts of elementary probability theory and statistics might look at (Bandyopadhyay and Cherry 2011).
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Bandyopadhyay, P.S., Brittan, G., Taper, M.L. (2016). Introductory Remarks—Inferential Uncertainty. In: Belief, Evidence, and Uncertainty. SpringerBriefs in Philosophy(). Springer, Cham. https://doi.org/10.1007/978-3-319-27772-1_1
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