The simple question, what is empirical success? turns out to have a surprisingly complicated answer. We need to distinguish between meritorious fit and ‘fudged fit’, which is akin to the distinction between prediction and accommodation. The final proposal is that empirical success emerges in a theory dependent way from the agreement of independent measurements of theoretically postulated quantities. Implications for realism and Bayesianism are discussed.

Classical mechanics is empirically successful because the probabilistic mean values of quantum mechanical observables follow the classical equations of motion to a good approximation (Messiah 1970, 215). We examine this claim for the one-dimensional motion of a particle in a box, and extend the idea by deriving a special case of the ideal gas law in terms of the mean value of a generalized force used to define “pressure.” The examples illustrate the importance of probabilistic averaging as a method of abstracting away from the messy details of microphenomena, not only in physics, but in other sciences as well.

What has science actually achieved? A theory of achievement should (1) define what has been achieved, (2) describe the means or methods used in science, and (3) explain how such methods lead to such achievements. Predictive accuracy is one truth-related achievement of science, and there is an explanation of why common scientific practices (of trading off simplicity and fit) tend to increase predictive accuracy. Akaike's explanation for the success of AIC is limited to interpolative predictive accuracy. But therein lies the strength of the general framework, for it also provides a clear formulation of many open problems of research.

Sober (1984) has considered the problem of determining the evidential support, in terms of likelihood, for a hypothesis that is incomplete in the sense of not providing a unique probability function over the event space in its domain. Causal hypotheses are typically like this because they do not specify the probability of their initial conditions. Sober's (1984) solution to this problem does not work, as will be shown by examining his own biological examples of common cause explanation. The proposed solution will lead to the conclusion, contra Sober, that common cause hypotheses explain statistical correlations and not matchings between event tokens.

There is an interesting problem concerning component causes posed by Cartwright (1983) in her book How the Laws of Physics Lie, which is easily explained in terms of a simple example. Consider a cup sitting on the table. Why doesn’t it move? The explanation given by Newtonian mechanics is that the cup is experiencing two forces-the downward force of gravity and the upward ‘elastic’ force of the table-and these two forces exactly cancel to produce a zero resultant force. This zero resultant force then produces a zero acceleration, which ‘explains’ why the cup doesn’t move. This rather simple, yet typical, example of theoretical explanation raises a surprisingly deep puzzle: What justification is there for believing in the existence of the component forces if they are redundant to the explanation? For it does not really matter what size the component forces are so long as their resultant is zero.

Skyrms's formulation of the argument against stochastic hidden variables in quantum mechanics using conditionals with chance consequences suffers from an ambiguity in its “conservation“ assumption. The strong version, which Skyrms needs, packs in a “no-rapport” assumption in addition to the weaker statement of the “experimental facts.“ On the positive side, I argue that Skyrms's proof has two unnoted virtues (not shared by previous proofs): (1) it shows that certain difficulties that arise for deterministic hidden variable theories that exploit a non-classical probability theory extend to the stochastic case; (2) the use of counterfactual conditionals relates the Bell puzzle to Dummett's (1976) discussion of realism in quantum mechanics.

Section 2 will begin by formulating Reichenbach’s principle of common cause in a more general way than is usual but in a way that makes the idea behind it a lot clearer. The way that Salmon has pushed the principle into the services of scientific realism will be explained in terms of an example, van Fraassen objects, Salmon modifies his stand and van Fraassen rejoins - all in section 2. (See van Fraassen 1980, chapter 2).

In this episode I think van Fraassen right in claiming - against Salmon that there is no categorical imperative for common cause explanation, and I add my own examples in section 3. The first example ‘is the explanation of the correlation between the equilibrium positions of two objects on a balance in terms of their property of “mass” and the law of moments.