This came up in a blog discussion a few years ago. As I wrote at the time:
Yeah, people always think I’m being sarcastic or patronizing, but I’m dead serious here. I see this so much, that researchers have strong beliefs and, at least from their perspective, deep qualitative understanding, but instead they write really weak quantitative papers based on bad data and sloppy analysis. No need for them to focus on their weaknesses.
But then they couldn’t “bask in the glorious objectivity of science”! (to quote IJ Good)
So the posterior is a restatement of the prior. And in between goes…what?
Perhaps a couple paragraphs about how other smart people previously stated something than can be construed as support. And then five paragraphs of self-aggrandizing, since the only real goal of the paper is to establish the first author as an autodidact polymath whose viewpoints are indistinguishable from fact.
I am currently trying to see how far I can come with writing a paper and concerning that I am reading stuff about psychopathy. I came across a classic book about psychopathy by Cleckley titled The Mask of Sanity, which if I understand things correctly still holds value concerning the topic of psychopathy. It’s considered a “classic” if I understood things correctly, so perhaps it already has had value.
On page 20 he mentions how he will follow a procedure of 1) the observation and recording of facts, 2) the grouping of these facts with proper correlation and with proper distinction from other facts, and 3) the effort to devise some summarizing or, if possible, explanatory statement which will enable one to grasp conveniently their significance.
Perhaps this is in line with the post above in the sense that there are perhaps many ways in which one can contribute to science, and many of these ways do not have to involve complicated data collection and analysis using statistical software and computers, etc.
Check out Robin Hanson’s paper on alien civilizations. One of the purest forms of the assumptions-based research genre.
Mark:
Without commenting on that particular paper, which I have not seen, let me emphasize that I have no problem at all with scientific papers that are purely speculative, as long as they are labeled as such. My problem is when people claim evidential support that isn’t there. One way to see that a paper’s claimed evidential support is illusory is when, after the evidence is shown to be wrong, the author states that their conclusion has not changed.
The Oxford dictionary defines “science” as: “the systematic study of the structure and behavior of the physical and natural world through observation, experimentation, and the testing of theories against the evidence obtained.”
A paper based entirely upon speculation may have value, but it is not science by definition.
Guessing at an explanation for something is the first step of science.
But the next steps of deducing a prediction from your explanation, then testing it, are also required to make it science.
My recollection is that Hanson’s paper is based on observations such as the speed of light being a hard limit. Einstein did a lot of work via thought experiments founded on the same thing. Was Einstein not doing science then?
I assume its about this paper:
https://iopscience.iop.org/article/10.3847/1538-4357/ac2369
That is speculation, which is fine, but then you need to derive some kind of prediction from it.
Einstein assumed constant speed of light for special relativity, which was kind of an early toy model that omitted gravity. Then in the presence of gravity, he said the speed would slow down as a function of the gravitational potential. Thus you would get apparent deflection of light from distant stars during an eclipse, so it was testable:
https://gutenberg.org/cache/epub/5001/pg5001-images.html#xd29e1904src
Btw, afaict if you look around the internet there are many people claiming GR predicts a constant speed of light, but this was *not* Einstein’s interpretation. It was that of his assistant, who basically restarted GR research in the 1940s:
https://en.wikipedia.org/wiki/Peter_Bergmann
Thus it seems what gets called GR these days comes from Bergmann, not Einstein per se. And the constant speed of light appears to be a point they disagreed about.
With the number of studies where major errors were found to “not change the findings of the study”, perhaps we can estimate the proportion of science (or scientists) who actually do just promote their priors as “science”. Perhaps the retraction watch database has some information on the frequency of this excuse.
Aren’t there calculations which are used to support the conclusion that there are too many statistically significant results in science using NHST based on what is to be expected given the statistical power of tests used (or something like that).
Perhaps you could take the differences between the percentages of observed and expected significant findings as an indication of the proportion of science (or scientists) who actually just promote their priors as “science”…
Maybe they should just write some papers about their priors and not mess around with actual data!
I don’t think it needs to be one or the other but I do think that it would be good if people in general spent more time thinking about what their priors are, and how their research might evolve differently if they had different priors.
I know there’s been some research projects where “teams” of people with different viewpoints work together to address a research question. But there the focus isn’t on priors but on conclusions. More useful, imo, is for teams with different conclusions to first examine whether their priors are different (which would then, logically, lead to different conclusions).
This is, imo, somewhat analogous to the conflict resolution paradigm of differentiating between positions and interests, where the positions are like conclusions and interests are like priors. Establishing where priors are similar or different can be very instructive, imo, for different answers to research questions just as can identifying where interests are shared (or at odds) can help explain different answers to policy questions.
We are all subject to cognitive biases based on our priors. But very few of us are very skilled in evaluating our priors can predict our conclusions.
But then aren’t there plenty of such papers in the (soft) social sciences? They tend to call that “theory”…