Scientific Regress
firstthings.com
firstthings.com
A lot of the problems in modern science revolve around the growing focus on bringing large quantities of research funds to your institution (which gets a large cut). Getting grants has an increasingly large need for political clout due to peer-review and increased competition due to reduced funding. Consequently, labs are getting larger and more hierarchical (i.e., an increased number of long-term post-docs and research scientists). As the organizational structure of science becomes less flat, the influence of those at the top is just reinforced and tends to constrain dialogue to fit established ideas. I think that the long-term progress of science depends on placing small bets on a greater variety of ideas rather than doubling down on fewer. Unfortunately, it will always be perceived to be safer to fund conventional ideas. Peer review enforces short-term, safe bet approaches.
Large labs can churn out lots of papers, even if they are relatively financially inefficient. One way to correct this (if you agree that is a problem) might be to normalize grant scores by previous grant funding to the PI. i.e., (X papers of Y impact)/Z dollars of funding over the past 10 years. Double-blind review of grants might also help, but blinding is relatively easily circumvented.
One way to bypass this would be for larger organizations to internally incentivize their researchers to cite their own organizations papers in preference to, or in addition to, other organizations papers. Thus the larger the organization, the more it can press down on the PI scales.
Alternately, even if it's not a several large organizations, smaller organizations can incentivize citing partner papers in the same way.
Which is just restating the concept that if you measure something, and then base monetary rewards off that measurement, then that number will be hyper-optimized (even if it's not a good predictor of future performance).
I don't have a good alternative, but just mentioning that there are easy circumventions (like you mentioned with double blinding)
The solution to science is actually very simple - move from a peer review ranked grant allocation system (which is totally gamed by those at the top) to a basic screen and lottery system. The idea is to do a basic screen on grant applications to make sure they are scientifically viable and not majorly flawed (at least 75% of grants should pass this test) and then put them all into a pool and draw winners from this pool until you have allocated all the money you have.
Lets stop using a system that can’t actually do the job (peer review can't separate the top 10% from the top 20%), and which is open to corruption and old boy networks, and move to one that is at least fair and better than all the alternatives.
The second problem is if you split the money too much then you won’t actually be able to do any research. To see an experiment through to completion requires a not insignificant amount of money (in most cases). If you start to hand out amounts of only a few thousand dollars at a time then nobody would be able to get any done.
I addressed the second point in my original post but maybe that was unclear: if there are so many applicants that individual grants become too small to serve their purpose, you stop accepting applicants. But then you require the applicant pool to spend some portion of their time growing the grant pool until the waiting list is empty. Flipping burgers if need be. Although we're talking about PhDs so I am sure there are better ways to use that labor pool.
As for favoring the old, all you have to do is require people to regularly re-apply. There's no reason you need to hop the line after the end of your term.
And you seem to be assuming that the waitlist will be prohibitively long, but the longer the waitlist the more labor you have for growing the grant pool, so it naturally self-regulates.
As much as I think the essay is great, this paragraph is a terrible ad-hominem argument for the existence of a "cult of science" or why it is dangerous. It doesn't matter if people wear xkcd shirts, Matt Damon uses profanity with "science", or if people like a facebook page. Laypeople having fun with "science" does not make a cult. A better argument would be to show how people cargo-cult science, and why it causes problems with science. The author fails to do both.
IFL is dangerous not because it is only pictures of scientific phenomena, but that bullshit is constantly posted on the page to be consumed by laypeople. The cult of science is dangerous because it has failed to teach people how to properly evaluate claims, and believe anything with the word "study" in it. Woo-pushers have appropriated the vocabulary of science indistinguishable to a layperson. Andrew Wakefield resurrected long dead pandemics by falsely publishing in the Lancet. This is what makes the cult of science dangerous, not the word "bitches".
>This is what makes the cult of science dangerous, not the word "bitches".
Nobody said that, so there's no need to burn your strawman there.
Science is all about the method and proper use of critical thinking, so you could assume it is a direct contradiction to have a vapid attitude of shitty reposts of forced-meme-tier macros that are often inaccurate, without trying to think about it an instant because it's nice virtue signaling (IFL in a nutshell). But you're right, he could write it explicitly.
There's a nice writeup of this problem on the language log [0], arguing that science is basically filling the role of biblical parables.
>Woo-pushers have appropriated the vocabulary of science indistinguishable to a layperson.
They are not responsible for that, and honestly, nobody is. Recently, I read the description of some machine learning algorithm that was filled with buzzwords and dubious physics analogies to a point that I thought it was a clever Sokal, but after some reading all of it was genuine. That's just how jargon works, you assume that the one who using it understands what he is saying, as long as he's using it seemingly properly, but you can't know unless you have a sufficiently good grasp of the semantics.
[0]http://itre.cis.upenn.edu/~myl/languagelog/archives/003847.h...
I don't think machine learning was a good place to pick an example from. A lot of so-called explanations of ML algorithms basically are Sokal hoaxes, and the fact is that the writer doesn't understand what the algorithm does and how.
One day a former grad student showed him some pictures.
"That's my lab!" He said.
"No, that's the replica I built of your lab in China for 1/100 of the cost."
Science is such a noble pursuit. If only it were separable from humanity's endless supply of greedy pricks.
It's perfectly normal if you're behind to copy the frontrunner and add your own improvements.
"We have learned a lot from experience about how to handle some of the ways we fool ourselves. One example: Millikan measured the charge on an electron by an experiment with falling oil drops, and got an answer which we now know not to be quite right. It's a little bit off because he had the incorrect value for the viscosity of air. It's interesting to look at the history of measurements of the charge of an electron, after Millikan. If you plot them as a function of time, you find that one is a little bit bigger than Millikan's, and the next one's a little bit bigger than that, and the next one's a little bit bigger than that, until finally they settle down to a number which is higher.
Why didn't they discover the new number was higher right away? It's a thing that scientists are ashamed of—this history—because it's apparent that people did things like this: When they got a number that was too high above Millikan's, they thought something must be wrong—and they would look for and find a reason why something might be wrong. When they got a number close to Millikan's value they didn't look so hard. And so they eliminated the numbers that were too far off, and did other things like that..."
I'm not quite sure what to think about the bias we might be seeing in this post from this site. This is quite a long essay and there's not a single link on a site that seems like it would be likely to be quite biased. I'm not saying anything is wrong with this, but I guess just consider the source? It could very well be fine.
(1) I originally had the thing stuffed full of citations and links, but since they wanted to print it in the paper magazine, we stripped all those out. If you want a reference for any particular claim, I'm happy to provide.
(2) First Things didn't pressure me to make any content or editorial modifications to the article. The sole exception was one or two more technical points that they asked me to cut for length and flow concerns (and because many readers don't have a quantitative background).
Any bias in there is purely mine.
It may be a perfect example of the "secular bias" the publication references, but I too groaned inwardly when I went to check out the "about" section after completing my read. Not because I changed my mind on the content of the article, but because I was imagining sharing it and having to deal with an argument about publication bias (in the media) for an essay about (among other things) publication bia (in the sciences)
Seems to be from: http://www.nature.com/nature/journal/v483/n7391/full/483531a...
My grades were swell, I still loved science - but I couldn't be honest with myself that I was going to be a real scientists in the end.
So here's what typically goes wrong: You get a lab assignment. Somewhere along 4th hour or second week you screw up and grab the lab ass for help. He says "sorry - just keep going."
You won't get a do-over. You can't afford to start over because the expected competence requires immediate good results.
Your lab time is limited and resources are scarce. If you want the grade you'd better "learn from your mistakes" and "be more careful next time."
What about the results? Well you already have a pre-conceived notion of what they should be. Maybe you get them from your mates, or look them up.
Learning from our mistakes is "science code language" for pushing small known nudges and data "massaging" as an acceptable method for passing the course.
Perhaps at one time students really did learn and grow from these mistakes - but the modern concept of failing is simply a quick exit from a highly competitive major.
All the while my own scientific rigor which I was supposed to be enforcing on my own results was slowly corrupted.
I couldn't truly say that my practise in the scientific method was honestly the truthful result of my own observations and methods. Perhaps I was just too anal at the time - so be it.
I worked in an environmental testing lab and the sloppy procedures practiced were sometimes much the same.
Circumstantial? Yes. Take of it what you will. But if my experience is like that of others - such corruption as reported here doesn't surprise me.
Science should be about failure as much as it is about success. Failure is a valid result - but we often fail to oblige real and honest failure as scientifically and (most importantly) educationally valid.
That was my draw to CompSci. Our very embrace of failure as a tool for learning. I absolutely love it.
I studied Engineering rather than a 'hard science'. My experience was the complete opposite. A failed experiment was almost seen as a good thing. It certainly gave you more to write about on your Lab Report. This probably highlights a lot of the difference between engineering and science. We care about Methods just as much if not more than the result.
>Your lab time is limited and resources are scarce. If you want the grade you'd better "learn from your mistakes" and "be more careful next time."
We were always encouraged to learn from failure and see it as an opportunity. My undergrad thesis was focused on synthesizing material for LI-Ion batteries using a technique called Electrostatic Spray Reductive Precipitation (ESRP). A large chunk of my writeup ended up being about the difficulties I encountered with the technique and how I'd reconfigure the experiment rather than the results I obtained.
I work at an industrial plant now and while plant trials aren't exactly laboratory science there is similar requirements when it comes to experimental rigour. You can be damn sure people won't cut corners because there are going to be a hell of a lot of questions asked if something from the trial can't be replicated when put into production.
First, I think that the most prominent studies are the most likely to have issues; generally they're doing something new, often with new methodology. It's here where extrapolations tend to be made that are the most dangerous. It's also not surprising that social sciences have such issues; they don't have rigorous tools (i.e. genetics) that can effectively ground their work. It makes compounding issues much harder to catch.
Related is the re-testing issue; in many fields, subsequent work will catch errors in previous work. If you work with a mutant and then someone else works with it, they'll see if it behaves differently than expected from the previous work. Germplasm travels, and it's the ultimate arbiter of truth. This usually does lead to further scrutiny and fixing the issue. The real problem that I've observed isn't that the errors aren't caught, but that a formal retraction isn't always done. Sometimes it just gets contradicted in a subsequent paper (and often with some relish) without ever resulting in a retraction. The editors of that journal clearly have a responsibility here that they are failing to uphold.
However, despite these faults, it's quite clear to those of us with 'boots on the ground' that you can't hide from the data; as long as you're using solid genetics and doing 'real' experiments (e.g. western blots, in situ/immuno-localization, simple gels/pcr, etc.) you can only hide for so long. The exception is if no one keeps working on it, in which case it's probably not that interesting to begin with. This also leads to a deep suspicion of bioinformatics among geneticists because we see how often things go wrong, and what is needed to make it right. Fortunately, genetics can still be used to great effect.
Ultimately, I'm not suspicious of the large body of work in my field. Most of it is based on extremely solid forward genetics that withstands lots of testing. Even now, great value is placed on these 'old school' methods because of how robust they are known to be.
Sure, statistics are difficult and can lead to incorrect conclusions, but that's why we make sure that a scientific claim is falsifiable. The fact that we can test the claims are where much of the power lies. Let's not forget that a few centuries of the scientific method have made human lives so much better than millennia of religion.
In that long (but excellent essay) Scott points out that self organizing systems like human society can sometimes arrange themselves in horrible local minimas which can be very difficult to escape.
I think the way modern science is organized is decidedly suboptimal. Note that I'm talking purely sociologically: science, as a method, is still by far the best thing we've developed to understand the natural world. However, the incentives around publishing / grants / hiring work are broken in a myriad of ways, from small to massive.
For example:
- High prestige journals/conferences are basically a crapshoot: http://blog.mrtz.org/2014/12/15/the-nips-experiment.html - consider that getting a paper accepted in NIPS/published in Nature might completely change the way your career shapes up.
- Grant funding is incredibly competitive, and the way they are awarded is also a crapshot: https://psmag.com/why-the-national-institutes-of-health-shou...
- The pressure to publish/obtain grants drives people to either make up data to obtain fancy publications, or even to kill themselves: http://www.dcscience.net/2014/12/01/publish-and-perish-at-im...
- We reward researchers for brilliant clean discoveries, not brilliant methodologies. However, the outcome of a serious research project is the one thing that a scientist cannot control: science investigates the unknown, if someone explores a plausible hypothesis in a clever way, and the hypothesis turns out to be wrong, the scientist didn't do anything wrong.
- PhDs are often used as cheap labour. A very famous PI once bragged that before they got their new fancy robot, they just had 10 chinese postdocs handle all the plating.
- Private companies make an insane amount of money from publishing scientific research that's carried out with public money, and that's reviewed by scientists (largely) paid with public money who donate their money for free.
I could go on for ages - and so could almost every researcher/ex-researcher on HN. There's a few no brainer changes - but the problem is that almost everyone in power who could affect change stands to benefit from the current system.
1: http://slatestarcodex.com/2014/04/28/the-control-group-is-ou...
>"What it really means is that for each of the countless false hypotheses that are contemplated by researchers, we accept a 5 percent chance that it will be falsely counted as true—a decision with a considerably more deleterious effect on the proportion of correct studies."
The hypotheses that the statistical "hypothesis testing" framework is usually applied to often amount to "two groups of cells/animals/people are samples from exactly the same population". Then there will be assumptions about the distribution of this population, etc. As has been noted by many (see eg Meehl 1967), such a hypothesis is pretty much always false and nothing but a strawman.
http://www.psych.umn.edu/people/meehlp/WebNEW/PUBLICATIONS/0...
You can assemble a thousand followers, claiming that electrons are little golden dwarfs, running through the metal if fed with potatoes, but the world wants chips without chips, so what hinders science? Globalization, border-lessness, in a ironic twist, cause where can that industrial revolution flourish and reward overcome all prejudices? When the potato religion/ideology is the same everywhere, there is noway to run too for the first battery maker.
One of the charlatans is not one. Better to feed a hundred of them, for the one to prevail.
If that was happening, it should be quite easy to measure in some areas: 5 year cancer survival rates should fall, corn yields should drop, and so on. For the most part, we don't seem to see that.
If it was all some sort of faith, and the bad findings were accepted, we should see more-or-less random changes instead of steady improvement
He cites all well-known and not at all surprising problems with peer review, which means that a sizeable proportion of published science is wrong -- wrong as in somewhat innaccurate and likely perfectible, and in now way as wrong as only religious ideas can be, i.e. completely false and unfounded. Asimov wrote a great essay about this Relativity of Wrong [1].
Indeed, that fact that science isn't regressive, and our knowledge is getting more accurate with each passing day, is self-evident. Suffice to compare what we have now with what we had 20 years ago, and its clear that we are not regressing.
Now, as to the specific examples that are falsely used to proove how wrong this wicked science can get:
1. "one hundred published psychology experiments".
Seriously, anyone in the field knows that these studies are rubbish, and all correlations are obtained after running so many statstical tests that they are clearly due to chance.
2. "half of all academic biomedical research will ultimately prove false".
Again, this is obvious. Many initially positive findings will be due to chance, especially when we are talking of small, exploration studies. Yes, many initially promising molecules did not live out to the hype, but others did, and spectacularly so. For instance, progress in oncology has been amazing lately. People who had life expectancies of mere weeks in early 2000s now routinely survive for years with their metastatic cancers.
3. "but are unlikely to mention a similar experiment conducted on reviewers of the prestigious British Medical Journal." [as opposed to Sokal's Social Text hoax].
Apples and oranges. Sokal's article is plain old gibberish, whereas the experiments in medical litterature were to assess the reviewers capacity to assess for methodology failures of otherwise plausible studies. Most of these methodology failures are weeded out by dedicated statistical and clerical staff.
Indeed, I would argue that some of those methodological problems were pretty minor, e.g. :
"Poor justification for conducting the study; No ethics committee approval; Failure to spot word reversal in text leading to wrong interpretation of results; No explanations for ineligible or non-randomized cases; Safety. No mention was made of monitoring patients for untoward effects; Format. The abstract was not written in the structured form requested by Annals; References. No reference cited was more current than 1989 despite the fact that there were numerous more recent studies; 5.Presentation. There were multiple grammatical and spelling mistakes, including the misspelling of propranolol as “propanalol." [2,3]
[1] http://hermiene.net/essays-trans/relativity_of_wrong.html
[2] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2586872/
[3] http://www.annemergmed.com/article/S0196-0644(98)70006-X/ful...