I teach an undergraduate class in ecology and every week or two I have the students in that class read a paper from the primary literature. I want them to learn to extract important information and to critically evaluate that information. This involves distinguishing evidence from inference and identifying assumptions that link the two. I’m just scratching the surface of this process here, but the detail I want to emphasize in this post is that I ask the students to describe the scope of the inference. What was the sampled population? What conclusions are reasonable based on this sampling design? This may seem straightforward, but students find it difficult, at least in part because the authors of the papers rarely come right out and acknowledge limitations on the scope of their inference. Authors expend considerable ink arguing that their findings have broad implication, but in so doing they often cross the line between inference and hypothesis with nary a word. This doesn’t just make life difficult for undergraduates. If we’re honest with ourselves, we should admit that it’s sloppy writing, and by extension, sloppy science. That said, I’m certainly guilty of this sloppiness, and part of the reason is that I face incentives to promote the relevance of my work. We’re in the business of selling our papers (for impact factors, for grant money, etc.). Is this sloppiness a trivial outcome or a real problem of the business of selling papers? I think it may lean towards the latter. Having to train students to filter out the hype is a bad sign. And more to the point of this post, it turns out that our failure to constrain inferences may hinder interpretation of evidence that accumulates across studies.
For years my work to encourage recognition of constraints on inference has been limited to my interaction with students in my class. That changed recently when I heard about a movement to promote the inclusion of ‘Constraints on Generality’ (COG) statements in research papers. My colleagues Fiona Fidler and Hannah Fraser made the jaunt from Melbourne over to the US to attend ESA in August (to join me in promoting and exploring replication in ecology), but they first flew to Virginia to attend the 2nd annual SIPS (Society for the Improvement of Psychological Science) conference where they heard about COG statements (there’s now a published paper on the topic by Daniel Simons, Yuichi Shoda, and Stephen Lindsay). In psychology there’s a lot of reflection and deliberation regarding reducing bias and improving empirical progress, and the SIPS conference is a great place to feel that energy and to learn about new ideas. The idea for a paper on COG statements apparently emerged from the first SIPS meeting, and the COG statement pre-print got a lot of attention in the 2nd meeting this year. It’s easy to see the appeal of a COG statement from the standpoint of clarity. But there’s more than just clarity. One of the justifications for COG statements comes from a desire to more readily interpret replication studies. A perennial problem with replications is that if the new study appears to contradict the early study, the authors of the earlier study can point to the differences between the two studies and argue that the second study was not a valid test of the conclusions of the original. This may seem true. After all, whenever conditions differ between two studies (and conditions ALWAYS differ to some extent), we can’t eliminate the possibility that the differences between the two studies result from the differences in conditions. However, we’re typically going to be interested in a result only if generalizes beyond the narrow set of conditions found in a single study. In a COG statement, the authors state the set of conditions under which they expect their finding to apply. The COG statement then sets a target for replication. With this target set, we can ask: What replications are needed to assess the validity of the inference within the stated COG? What work would be needed to expand the boundaries of the stated COG? As evidence accumulates, we can then start to restrict or expand the originally stated generality.
In a COG statement, authors will face conflicting incentives. Authors will still want to sell the generality of their work, but if they overstate the generality of their work, they increase the chance of being contradicted by later replication. That said, it’s important to note that a COG doesn’t simply reflect the whims of the authors. Authors need to justify their COG with explicit reference to their sampling design and to existing theoretical and experimental understanding. A COG statement should be plausible to experts in the field.
I started this post by discussing the scope of inference that’s reasonable from a given study, but although this is clearly related to the constraints on generality, a COG statement could be broader than a statement about the scope of inference. Certainly as presented by Simons et al., COG statements will typically expand the scope of generality beyond the sampled population. I haven’t yet resolved my thinking on this difference, but right now I’m leaning towards the notion that we should include both a scope of inference statement and a constraints on generality statement in our papers, and that they should be explicitly linked. We could state the scope of our inference as imposed by our study design (locations, study taxa, conditions, etc.), but then we could argue for a broader COG based on additional lines of evidence. These additional lines of evidence might be effects reported by other studies of the same topic, or might be qualitatively different forms of evidence, for instance based on our knowledge of the biological mechanisms involved. Regardless, more explicit acknowledgements of the constraints on our inferences would clearly make our publications more scientific. I’d love to have some conversations on this topic. Please share comments below.
Before signing off, I want to briefly mention practical issues related to the adoption of COG (and/or scope of inference) statements. Because scientists face an incentive to generalize, it seems that a force other than just good intentions of scientists may be required for this practice to spread. This force could be requirements by journals. However, many journals also face incentives to promote over-generalization from study results. That said, there are far fewer journals than there are scientists, so it might be within the realm of possibility to convince editors, in the name of scientific quality, to add requirements for COG statements. I can think of roles that funders could play here too, but these would be less direct and maybe less effective than journal requirements. I’m curious what other ideas folks have for promoting COG / scope of inference statements. Please share your thoughts!
2 thoughts on “Is overstatement of generality an Open Science issue?”
thanks very much for sharing these ideas. I agree with a COG (or Limitations) section at the end of every paper. I wonder however, if it should be done in a more systematic way, as in for example a ‘structured abstract’ rather than a free paragraph? I think a systematic version may help reviewers to have a better evaluation of the limitations.
Also, I just came across this post by Andrew Gelman, it is somewhat related to your post:
Quality control” (rather than “hypothesis testing” or “inference” or “discovery”) as a better metaphor for the statistical processes of science
Thanks again. All the best,
Thanks Diego. I think you’re right that this would work best as a dedicated section of each paper – possibly part of a structured abstract.
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