A while back we invited you to ask us anything. Here are our answers to our next question, from Pavel Dodonov: should ecological research always be prediction/hypothesis-driven, or should there be more space for descriptive research?
Jeremy’s answer: The question of whether there should be “more space” for descriptive research does puzzle me a little, because there’s never been more space for it than there is now! It’s never been easier to publish descriptive, hypothesis-free research (or any sort of research). That’s what megajournals like Plos One are for–to publish anything technically sound. And if you just want to get your work out there for others to evaluate for themselves without having to go through peer review, well, that’s what preprint servers are for.
Of course, there’s no guarantee that others will pay attention to your descriptive work after it’s published, but that’s a different issue. I think the science that gets the most attention ideally should be the work that answers important, interesting questions, and that identifies new important, interesting questions. Which will sometimes be descriptive work (e.g., Brian’s recent very high-profile collaborative work describing local-scale changes in biodiversity), but sometimes not. Rather than worry whether we pay enough attention to work using any particular methodology (descriptive work, hypothesis-driven work, quantitative work, field work, work to develop new methods, or whatever), I think it’s better to ask whether science’s “attention concentration” mechanisms concentrate attention on the work that most advances our understanding of important, interesting questions.
Brian’s answer: A lot of people have strong opinions on this. Many graduate programs treat hypothesis driven research as a mark of research quality. So does NSF (good luck getting a proposal admitting you are going to do primarily descriptive research funded). But I am very blase about the distinction. I prefer much more to think about the questions and where the questions come from. Good questions make good science. As an example, most of you have probably taught or at least TAd a class where students are supposed to come up with a hypothesis and test it (because that is what we pretend good scientists do). But the hypothesis is usually very uninformative (I’ll add this to that system and response variable Y will increase). It is a hypothesis, but it is not a good question. What makes good science is a really good question that comes from a really good knowledge of the system, a lot of reading of the literature, and a creative mind. Those three tools can combine to do good hypothesis-driven or descriptive research. And many good questions are inherently descriptive.