New Article: How to Spot Fake Science

@Christy wrote up some helpful tips to “spot the conspiracy.” What red flags do you look out for?

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Great article. Thanks! I am sharing it with family

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Thanks for the article, Christy. There are indeed some nuances that require discernment to tease apart. Just to look at a first one …

  • Explanations are made up after the fact to fit whatever outcomes are observed.

I can hear anti-evolutionists responding: this is what “evolutionary” scientists are doing all the time! And indeed, other than a slight opprobrium in some of the word choice here (“made up”), it is fair game, I think, to note that this does border on the necessary enterprise of making sure an existing theory fits the facts at hand, which is a rightly-celebrated and legitimate scientific process.

Could there be more discussion about teasing this apart? I think one part of the difference might rest in how comprehensively some theory covers the facts. Most conspiracy theories are made up to ideologically answer one specific set of facts, and fall apart when examined for how many other facts they can cover vs. all the awkward assumptions needed to prop them up. Whereas a well-developed scientific theory may have sticking points here or there to be resolved, but have a strong showing for getting most other stuff pretty well explained. Thoughts?

The point was to contrast with the scientific method of stating a hypothesis first, and then explaining how observed outcomes do or do not confirm the hypothesis. With lots of pseudoscience, (think a fad diet), the outcome is explained retroactively to fit a belief. If you lost weight, it was because the magic diet works. If you didn’t lose weight, there is some other explanation offered, like your metabolism was not yet properly reset to benefit from the effects of the detox or whatever. Everything just confirms the belief, nothing disconfirms it.

With evolutionary science, the model makes predictions and observations either fit or don’t fit. In either case, scientists have to explain the outcomes with reference to the model, they can’t just make something else up. I don’t think modifying a model in light of evidence is the same thing as “making up an explanation.”

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Enjoyed the article. A good one to share on Facebook. It is frustrating when you see an article, and the source referenced is any other pseudoscience site, which references yet another pseudoscience site and you can never find an original reference in that circular world. Even Googling gives only references to the network of pseudoscience or politically biased sites.

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I think a key difference here is consistency. Science can deal with the past, contrary to the claims of those who deny that historical sciences are real science (a claim found not only among young earthers but also among egotistical experimental scientists and oversimplified “scientific method”-focused grade school science lessons). Pseudoscience uses post hoc claims to deal with one issue without regard to whether they are consistent with claims used to explain something else.

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Wow. This needs to be more widely read. I especially liked this point but the how-to spot lists are great.

“It’s not being skeptical to automatically assume the media or scientific experts are lying. That’s just being cynical.”

Now how can we get this into a Trojan horse which people will actually consider … wherever they may be on political spectrum?

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I have recently dipped my toes into bioinformatics and “big data” sets, and what you describe is one of the big dangers in that field, especially with the new algorithms and databases that have been recently developed. Statistics and understanding false discovery rates are important, but your initial findings are often data first, hypothesis second.

The way to overcome this is to test your new hypothesis from a different angle. For example, if you want to show that Drug X causes specific changes in gene expression then you need to test the steps in the causal chain that you initially hypothesize. This may include gene knockouts, tracking the phosphorylation of specific proteins, or measuring the activity of single proteins in combination with the drug. Bad or fake science often doesn’t take things that extra step, or even as far as an experiment with proper controls.

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Yes, I guess the key is that you can discover patterns by looking for them without a specific hypothesis in mind, but then it’s like the observed pattern becomes the hypothesis and needs to be further tested to see if it holds or is predictive of other patterns that have yet to be observed. Maybe this is part of why the medical consensus seems to fluctuate more than the consensus in some other fields. That and the fact that there are often so many variables that can’t really be controlled or are unknown.

I definitely feel that this is a point need a bit of refinement in the explanation. Because the reality is the scientist do start by making an observation then hypothesizes on why that observation occurred. The big difference, I feel is that a scientist will then try and disprove their own hypothesis by predicting the results of experiments were as pseudo scientists don’t.

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Outstanding article

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