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by olooney 2456 days ago
Scientists usually try to distance themselves by saying those are soft science or even pseudo-science. This leads to the embarrassment of the demarcation problem[1] which is that no one can give a bright-line rule[2] to distinguish between the "real" science and pseudo-science. All of the demarcation criteria that have been proposed (such as Popper's falsifiability[3]) are inadequate in one way or another. In particular, they don't seem to capture the reasons a scientist would give about why a particular nutrition or social science paper is bad. The scientist would say things like, "Well, your sample size is small and not representative of anything except psych undergrads, you didn't control for age or gender, the participants and experimenters weren't properly blinded, you tested 15 hypothesis and only reported the p-value for the one that was under 0.05, and even that is wrong because you didn't apply Yate's continuity correction on your chi-squared test, AND NONE OF THAT EVEN MATTERS because the effect size you report is too small to be of practical consequence!" Nothing in there about the hypothesis not being testable; yet this is the kind of stuff that really separately the wheat from the chaff.

So we're left with a "No True Scotsman fallacy" where have to say that some science is "good" and some is "bad" and the only way to tell is to ask someone knowledgeable to evaluate each paper on a case by case basis. Not terrible useful to the layman.

And why do we want any kind of science to automatically get respect anyway? Good science is good because its already been subjected to an incredible degree of scrutiny. It will hold up to a little more. The real problem is disingenuous, bad faith arguments which are allowed to dominate the conversation. The real problem is to teach the general public to distinguish between sincere, good faith arguments and patent bullshit. This is much more difficult than it sounds because bullshit can easily conform to any merely superficial characteristics.

[1]: https://en.wikipedia.org/wiki/Demarcation_problem

[2]: https://en.wikipedia.org/wiki/Bright-line_rule

[3]: https://en.wikipedia.org/wiki/Falsifiability

4 comments

Why not have a general checklist with a minimum set of requirements for scientific papers that are relevant across all branches of science? The people putting their names on the paper would have to show that they followed everything or give reasons for skipping a step. The receiving journals would have their editors re-check the checklist. As part of reporting the results of the paper, the level of completeness of the checklist would also be in the report.

Yes, the checklist would not be all-encompassing or foolproof, and there would likely be revisions to the checklist, and maybe even domain-specific variants, but it would be an extra level caution that the media could report or choose to ignore at their will. Over time, the apparent level of scientific rigour would improve. No, it’s not bulletproof and there will be people who will try to meet the checklist and still present erroneous conclusions as reliable science, but it would be an improvement in the status quo for a layperson who is aware and values said checklist.

There are things like CONSORT[1] which kind of do this. Statisticians like Fisher[2] have a ton of good general advice of the design of experiments. (A plug for The Lady Tasting Tea[3] and the 7 Pillars of Statistical Wisdom[4] feels appropriate here.)

On the whole though, most of the things you should and should not do are so domain specific its very hard to give much useful advice at the level of "all science." Right now this seems to work because researchers are so eager to anticipate objections and and avoid unnecessary arguments during peer review they stick slavishly stick to the same methods used by seminal papers in their field, and this has the same effect as running down a checklist.

There probably is a case to be made for using an actual checklist, though[5].

[1]: http://www.consort-statement.org/

[2]: https://en.wikipedia.org/wiki/Design_of_experiments#Fisher's...

[3]: https://en.wikipedia.org/wiki/The_Lady_Tasting_Tea

[4]: https://www.goodreads.com/book/show/27311742-the-seven-pilla...

[5]: http://atulgawande.com/book/the-checklist-manifesto/

> which is that no one can give a bright-line rule[2] to distinguish between the "real" science and pseudo-science

"if you can't repeat and predict, stop calling it science" seems like a nice bright line.

Peer-review obviously isn't enough, I'd like to see peer-replicated studies become a thing.

What about astronomy, cosmology, archeology, paleontology, volcanology, evolutionary biology, cladistics, macroeconomics, etc., which do not allow us to "repeat and predict," as you say?

Some of these cases can be rescued by considering "retrodiction"[1][2] as valid substitute for prediction in the right circumstances, but not all.

I personally think the analysis of the Mott problem[3] points the way to the solution to some of these kinds of issues. That is, a prediction can take the form of a likelihood function which assigns high probabilities to certain combinations of events and low probabilities to others. Theories with low perplexity[4] can be considered correct even if they can't make predictions, and the study of such theories can be scientific. But as far as I know I am the only one who thinks so.

[1]: https://en.wikipedia.org/wiki/Retrodiction

[2]: https://afdave.wordpress.com/2007/09/04/sir-karl-popper-and-...

[3]: https://en.wikipedia.org/wiki/Mott_problem

[4]: https://en.wikipedia.org/wiki/Perplexity

> What about ... which do not allow us to "repeat and predict," as you say?

"if you can't repeat and predict, stop calling it science" seems like a nice bright line.

For what it's worth, there is science meeting the "repeat and predict" definition that can be done in each of the fields you list.

Astronomy / cosmology absolutely makes testable predictions.
Not that I necessarily agree, but the suggestion was to find a different word for these fields.

(Also, most of the fields you listed do allow for replication.)

I think a lot of what you are talking about is due to a huge amount of 'papers' being nothing but coloration tests. Again, I think terminology is of utmost importance when discussing public perception so I would rather not talk about this as 'science'. It's 'research'.

I don't see a "No True Scotsman fallacy" here because I think I define it quite clearly: Can you provide accurate predictions? I'll concede that there exists a bit of grey area in that question, but the answer is heavily bi-modal.

even statisticians call social science - psychology especially - a pseudo science