1.1.4. Deductive vs. non-deductive inference

Although all good reasoning is of interest to logic, we will focus on reasoning—and, more specifically, on inference—that is good in a special way. To see what this way is, let us begin with a rough distinction between two kinds of reasoning a scientist will typically employ when attempting to account for a body of experimental data.

An example of the first kind of inference is the extraction of information from the data. For instance, the scientist may notice that no one who has had disease A has also had disease B. Even though this conclusion is more than a simple restatement of the data and could well be an important observation, it is closely related to what is already given by the data. It may require perceptiveness to see it, but what is seen does not go beyond the information the data provides. This sort of close tie between a conclusion and the premises on which it is based is characteristic of deductive reasoning.

This sort of reasoning appears also in mathematical proof and in some of the inferences we draw in the course of interpreting oral or written language. It is found whenever we draw conclusions that do not go beyond the content of the premises on which they are based and thus introduce no new risk of error. It is this kind of reasoning that we will study, and the traditional name for this study is deductive logic.

Science is not limited to the extraction information from data. There usually is some attempt to go beyond data either to make a generalization that applies to other cases or to offer an explanation of the case at hand. A conclusion of either sort brings us closer to the goals of science than does the mere extraction of information, so it is natural to give more attention to an inference that generalizes or explains the data than one that merely extracts information from it. But generalizations and explanations call attention to themselves also because they are risky, and this riskiness distinguishes them from the extraction of information.

There is no very good term—other than non-deductive—for the sort of reasoning involved in inferences where we generalize or offer explanations. The term inductive inference has been used for some kinds of non-deductive reasoning. But it has often been limited to cases of generalization, and the conclusions of many non-deductive inferences are not naturally stated as generalizations. Although scientific explanations typically employ general laws, they usually employ other sorts of information, too, so they are not just generalizations. And other examples of inferences whose conclusions are the best explanations of some data—for example, the sort of inferences a detective draws from the evidence at a crime scene or that a doctor draws from a patient’s symptoms—will often focus on conclusions about particular people, things, or events and are not best thought of as generalizations at all.