This paper considers three common types of claim to research knowledge, and the relative difficulty of making each type of claim in an empirically and logically justified manner. Before this, the paper looks at some more general issues often raised when discussing knowledge in social science, such as the nature of truth and justified belief, the existence of “isms” and paradigms treated like fashion accessories that one can adopt or not at will, and the intrinsic limitations of how we get to know about the “stuff” we might want to make research claims about. The idea of this early section is to remove some potential obstacles, before arguing that none of these issues is relevant to the rest of the paper about the nature of claims in their most generic form, independent of things like specific methods of data collection. The first type of claim we identify is a fully descriptive one that only summarises the data observed. This is the easiest and safest kind of claim, but even these might suffer from non-random errors and inaccuracies. However, their biggest limitation is their lack of any wider utility. The second kind of claim is a generally descriptive one that makes statements about unobserved data on the basis of a fully descriptive claim. Here we meet Hume’s problem of induction. These claims have two parts, and the inductive part cannot seemingly be justified by logic, inferential statistics (whether Fisher or Neyman-Pearson style), Carnap’s inductive probabilities, or even necessarily by Popper’s falsification process. The third type is a causal claim, which we argue must also be a general claim. We develop a model, based on the work of Mill and Bradford-Hill, of what a plausible causal claim entails. But it still has all of the problems emerging from the first two types of claim, and adds a further problem created by our inability to assess causes directly. The paper concludes by suggesting how social science can proceed most safely in practice.