This shows why you have to explicitly quantify statistical power and error model for your study design. Tools are good, but not good enough to do it for you.
Then, do calculate effect size instead of a binary answer. You will get answer with a digit in fifth place with a proper size unbiased sample. Note that for proper genome study, the sample size for observational studies is in tend of thousands barring inbred model studies. (Mice or men, with easily detectable disease process.)
Science became a new religion. Instead of saying "shut up god made it that way" you say "shut up science says it's that way".
You can find hundred of studies to support your arguments, no matter what they are, it's even easier if you don't actually read the studies and stop at the summaries made by popular medias.
Science has certainly become a fantastic way to bullshit people. Combined with how science is funded now its created a weird status quo where things that wealthy interest groups want to be true will tend to have more studies which point to the desired conclusion. Something that's true can be less scientifically backed than something that's false if the truth isn't in peoples financial interests.
I also see a weird phenomena where I'll see people who are wrong like flat earthers running DIY scientific experiments. Yet this crowd seems to be thought of as generally anti-science when they're probably more scientifically curious than the actual population. Being "scientific" means subscribing to a certain set of consensus beliefs among scientists. You can not use science at all yourself directly and be considered a scientific person so long as you just believe in certain things.
> You can find hundred of studies to support your arguments, no matter what they are.
I think that simply incorrect, assuming 'studies' != Youtube videos. I know because I've been involved in internet arguments where I've gone looking for studies and found my own argument is incorrect/poorly supported.
I'd say it depends on the topic, and it depends on the standard you hold the papers in question to. For instance, I can find you a paper showing almost anything you like in the field of nutrition in terms of what people should eat. It'll even hold up in the abstracts. Whether it holds up beyond that can get complicated, though; I've lost count of things like "a study that X intervention is good in 20 rats over 5 days if you feed the X rats high-quality X but give the not-X rats low quality not-X" or n=8 human studies or n=20 self-reporting studies or all kinds of stuff like that.
In those exact words no, but if you break down the nutritional content of the Sausage McMuffin, yes, probably. A lot of studies are done on rats where even the rats eating "quality" food in the study are still eating things much lower in quality than a fast food muffin. That doesn't even sound particularly challenging.
To be honest, if you're trying to make my point sound absurd by exaggeration, you've shot way too low. The mainstream nutritional view would be that a Sausage McMuffin every morning on its own isn't going to be a particularly bad thing. You need to be a lot more specific with an overall diet to be a problem. You should have asked something like whether you could find a study about eating nothing but lard is available.
To which my reply would be that the principle of charity would make it clear that in general I meant any semi-realistic nutritional view is available, not that there are studies that prove humans can be healthy on a diet of rocks and asbestos. It really isn't to anyone's advantage if I also have to append to my little post a complete discussion of what is and is not within the boundaries of nutritional theories that have been studied.
Often the studies showing your argument is "incorrect/poorly supported" are poorly supported themselves, and just go uncontested for decades, piling citations.
And that's on harder sciences. In soft sciences (and liberal arts, etc) it's the wild west -- what gets more grants and is more popular at the moment gets priority and is "proved".
Yea... People have been even hoaxing journals for decades and it goes ignored. This scheme got some attention, probably just for how hilarious it is that they published a chapter of mein kampf in one of the leading feminist journals of gender studies: https://youtu.be/fvZNXRiAsn4
I don't see the comparison at all - "science" is saying that it is not the why in that context.
It is saying that it does and we have reason to think that. It isn't religion to say that it is stupid to deny that the blue footed booby exists. Even if it is wrong and asseerts the piltdown man is real it is markedly different from religion.
what you refer to is not Science,its pseudoscience. Pretty much any crap gets published these days, review standards are abysmal and there is hardly any replication.
Only thru replication can you actually claim there is Science.
The end result is the same. When a big tv channel says "Eating fat is definitely bad because this study says so" nobody will check the actual study and see that it was done on 3 mice for a duration of 10 days with completely unrealistic settings. It's still science, it's just misinterpreted or extrapolated.
I see it everyday on HN, people _believing_ we'll all migrate to Mars and terraform it by the end of the century which means climate change is a non issue. Or that we'll get fully autonomous cars by 2020 because Elon "Science" Musk said so and people _want_ to _believe_ in it even though absolutely nothing supports it. It really isn't much more than astrological predictions at that point.
People pick whatever "science" supports their make believe-world and go with it.
Not really true. Tesla is at the forefront of applying machine learning in real-world settings. It's definitely not unrelated to science, in my opinion. If autonomous driving in 2020 is on the timetable, Karpathy (Head of AI) is probably confident that it is possible. Musk is very aggressive on timelines (but always wraps it in "it is probable that", which to him is cautious but to which translates to almost certainly in newspapers), but from what I know, he has delivered most promises (which he sees more as projection of previous trends), albeit a little late.
There's probably some real science happening behind the scenes, experimenting with ML algorithms (Karpathy is certainly up to it). But the challenge of getting it to work sufficiently well in the real world isn't science, it's engineering. Meeting schedules isn't science, it's management. And so on. "Believing in science" has nothing to do with believing musk will or will not succeed, and I think you would find the vast majority of people who think he will succeed (me included if you don't mind late) don't attribute it to "because science".
I don't see the line between science and engineering as clearly as you do, apparently. Is CERN a science or engineering project? Drug design? Genuinely curious what qualifies as science. Seeing some articles, it's not the quality. Applied vs. fundamental also seems like a difficult line to actually draw.
Edit: Especially in ML, a large part of the research is done in companies.
Science is about discovering things via experiments and observations about the world, engineering is about making things that work. There is a tiny bit of overlap.
CERN is a gigantic engineering project used to do a bit of science. Experimenting with different concrete mixes to find one with a set of qualities is science used to let you do some engineering. OpenAI's dota bots are the sort of thing that might fall in the overlap of both discovering things and making things that work.
Maybe more to the point, "believing in science" means "believing that those experiments and observations reveal true facts", which has nothing to do with whether or not we believe Musk will succeed at his self driving car ambitions.
>what you refer to is not Science,its pseudoscience. Pretty much any crap gets published these days, review standards are abysmal and there is hardly any replication.
That's like the argument that USSR was not real communism, etc.
At some point, science is as science does.
There's no some holier, better checked, domain of practice. It is what it is, and it sometimes has replication, more often than not it doesn't.
One factor you cannot ignore is the exponential increase in the number of 'scientists.'
In times past, for better and for worse, college was generally relegated to an extremely small section of generally over-performing society. And of these a tiny minority would then go on to pursue the post-grad level education that would culminate in becoming a scientist. In today's society college has become high school 2.0. And to some degree post-graduate education is going down the same path. For instance today more than 1 in 8 people have some sort of postgraduate degree. [1] Sourcing that because it just sounds absurd. In other words today more people have a postgraduate education than the total that went to university in the 70s.
This has resulted in an exponential increase in the amount of stuff getting published as well as a simultaneous and comparably sharp decrease in the overall quality of what's getting published. So I would actually tend to agree with you. This cynical state of science is generally pretty accurate for the state of what passes as science today, but it was not always this way. 'Science' as a whole is in many ways reflective of the mean, and in the public mind even the lowest common denominator. And both of those have undoubtedly fallen far below what they were in times past.
I mostly agree, but the GP is also certainly correct though. There is in fact a "holier than thou" science and it is that which follows the scientific method. It is reproduced, empirical, fundamental science. Most garbage published in journals today does not meet that criteria, and even economists, psychologists and even sociologists call themselves scientists when they cannot possibly follow the scientific method in nearly every part of what they study.
I have a hard time believing that the issue is a dilution in the “quality” of scientists, but I would agree that ever-increasing competition for funds and jobs has produced some perverse incentives.
The consequences for publishing something that’s wrong but not obviously indefensible are often pretty low. On average, it probably just languishes, uncited, in a dusty corner of PubMed. It might even pick up a few citations (“but see XYZ et al. 2019”) that help the stupid metrics used to evaluate scientists.
The consequences of working slowly—-or not publishing at all—- are a lot worse. You get scooped by competitors that cut corners, and there’s not a lot of recognition for “we found pretty much what they did, but did it right.” Your apparent unproductivity gets called out in grant reviews and when job hunting. The increasing pace and career stage limits (no more than X years in grad school, Y as a postdoc, Z to quality for this funding) make it hard to build up a reputation as a slow-but-careful scientist.
These are not insoluble problems, but they need top-down changes from the folks who “made it” under the current system....
The replication crisis that's plaguing much of the social sciences, but especially psychology, did not cherry pick studies. It started with an effort to replicate studies only from high impact well regarded journals in psychology. [1] It found that 64% of the studies could not be replicated, leading to the curious outcome that if you assumed the literal and exact opposite of what you read in psychology (e.g. - what is said to be statistically significant, is not) - you would tend to be substantially more accurately informed than those who believe the 'science.' [1]
But more to our discussion, two of the journals from which studies were chosen were Psychological Science - impact factor 6.128, and the Journal of Personality and Social Psychology - impact factor 5.733. The replication success rate for those journals was 38% and 23% respectively. I'm certain you know, but impact factor is the yearly average number of citations for each article published in a journal. A high impact factor is generally anything above about 2. These are among the crème de la crème of psychology, and they're worthless.
As you mention pubmed, preclinical research is also a field with just an absolutely abysmal replication rate. And once again these are not cherry picked. In an internal replication study Amgen, one of the world's largest biotech companies, alongside researchers from MD Anderson, one of the world's premier cancer hospitals, were only able to replicate 11% of landmark hematology and oncology papers. [2] Needless to say those papers, and their now unsupported conclusions, were acted upon in some cases.
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All that said I do completely agree with you that the current system of publish or perish is playing into this, but your characterization of the current state of bad science is inaccurate. Bad science is becoming ubiquitous. However, I'm not as optimistic that there is any clean solution. There are currently about 400 players in the NBA. If you increased that 4,000 what would you expect to happen to the mean quality and the lowest common denominators? Suddenly somebody who would normally not even make it into the NBA is a first round pick. And science is a skill like any other that relies on outliers to drive it forward. We now have a system that's mostly just shoveling people through it and outputting 'scientists' for commercial gain. The output of this system is, in my opinion, fundamentally harming the entire system of science and education. And this is a downward spiral because now these individuals of overall lower quality are working as the mentors and advisers for the next generation of scientists, and actively 'educating' the current generation of doe-eyed students. This is something that will get worse, not better, over time.
Speaking of replication, my personal experience in very narrow field of audio DSP which is easy to test gave results of 9 papers impossible to implement mostly due to missing key details, 6 more where results only apply in specific test signals (total failure in reality), 3 where performance was overstated by over 12 dB in real samples. 8 were really good and detailed. Two had actual used test code available, one has it in printed form. None with the code were any good. :D
(IEEE database around 2005 in noise reduction, echo cancellation and speaker separation or detection.)
No, science is always science. Just because the media portrays certain things as "scientific truth" (or, for that matter, scientifically unsure) doesn't make it so.
Indeed, even if scientists claim something bogus, that doesn't make it science.
So...actually, yeah, it's a lot like the argument that the USSR wasn't real communism, any more than the Democratic People's Republic of Korea is democratic. People claim it to be X, other people take that claim as gospel and use it to paint X as terrible, despite the fact that the people making the claim are full of shit.
The argument that the USSR wasn't really communism isn't a semantic argument of "yeah it was a marxist utopia" but rather one of whether we support governments claiming to be communist. We don't have an example of successful communism, while we do have examples of successful science.
I have literally never seen that argument, and frequently seen the argument that communism (and/or socialism) is Bad, because the USSR was communist, and they were Bad.
Not that I'm saying you haven't encountered the reverse; I'm quite willing to believe that people who run in other circles (or make other claims) encounter different arguments. But yeah, I see the "we shouldn't want communism/socialism, it killed millions of people under Stalin" argument all the damn time.
>Depending on the definition of X, you CAN say that something is not X
That is a good strategy only if you already have a sample of the thing to derive a definition from.
To create a good definition you should examine reality, and see the thing as it actually behaves, first. Only then, once you have a reality-based definition, you can judge other specimens and use the definition to say whether they are X or not.
Else, you just impose some idealistic / non-empirical standards upon reality based on an arbitrary (since it's not based on observation) definition.
The land and the people existed (as a land and as a people) and gave its name to Scotland (and content to the definition), not the inverse. It wasn't someone making up the word first and others then checking whether the people in Scotland fit it.
>Only if you define 'science' as 'the thing that people labeled scientists do', can you arrive at your conclusion. I would define scientists as 'people practicing the scientific method'.
In real life, people call themselves and are called by others scientists if they have studied for and are employed as such, whether or not they "practice the scientific method" and even more so, whether or not they practice it properly.
So defining scientists as 'people practicing the scientific method' (and e.g. excepting people with Ph.Ds who practice it badly or care to get grants to the detriment of science) is rather the canonical 'no true scotsman' fallacy.
In that sense, no scientist could ever falsify data or make up a theory and cook its research to support it, or prove something that a company paid them a grant to prove, because "by definition" such a person wouldn't be a scientist.
The concept of science, which is the empirical study of reality, does not change. There are many concepts that can share the same word - is a Scotsman someone born in Scotland, one who moved there, one who shares Scotland's culture and ideals? There should be different labels for each of these concepts but there aren't.
The importance, relevance, trust, and reality of science may change, but the underlying concept does not. Nevermind all the other forces trying to co-opt 'science' for their own purposes.
How many papers and articles describe a purely empirical inquiry into reality and accurately describe all shortcomings and sources of error? 10%? 1? It matters that our trust in "science" may continue to degrade, but none of those change the underlying concept/ideal.
Please do find me hundred of studies to support objects on earth falling upwards... newton's gravity is pretty well established within the context it applies to... so in no way can science be compared to religion.
There is well established science and science-in-development, which can turn out to be wrong when new evidence is discovered. But for religion there is NO such gradient - everything is based on faith, evidence does not even come into play...
However, sadly, many people believe in much more questionably "scientifically" proven things, largely due to how simple scientific proof appears due to things like the absolute acceptance of gravity on earth. Going with the religion analogy, there are many basic facts in religious scriptures which are true but this does not make many of the more questionable statements such as how exactly the earth will be destroyed and where you go when you die to be true.
I completely agree with you that science done properly is not a religion; however, that is simply not the case for most of us who either through lack of care or time will never go beyond seeing a news article about a certain scientific discovery and believing in it due to a supposed scientific breakthrough published in a fancy sounding journal.
I agree with what you're saying in general, but there's also problems in play in academics that are broader than just bad statistics. The article tries to convey the nature of this problem, but the "chasing a unreplicable effect" or "science sometimes takes awhile to work itself out" is just the tip of the iceberg.
This touches close to home because it's in my area of research, and for years I had many discussions with colleagues about this very same genetic effect and its problems. This SLC6A4 candidate gene research was not just a fluke of incompetence (unless by incompetence you mean much of an entire biomedical field of researchers), and it persisted wildly, with huge amounts of methodological research and money behind it.
Papers advocating for this type of research (and even more statistically problematic research) were published in Nature, with lots of methodological arguments, by established quantitative experts. This doesn't mean it's correct, just that by all superficial indications, it was solid. You had to question these authority figures, and a body of research, in good journals, the actual nature of the argument, and even then you were branded a naysayer or curmudgeon.
Even when people started questioning the effect, then you had people start advocating for more complex interactions (as intimated at in the article) that just amounted to unintentional (or intentional) data fishing and p-hacking with a theoretical cover.
When I started pointing out how problematic this all was to my colleagues, I had some of them outright explain to me that they thought it might be bunk, but it was popular, and if they found something significant, and it landed them a paper in a prestigious journal, why wouldn't they publish it? That is, you gotta publish what's popular because that's how you build an academic career.
I can't begin to explain all the shady stuff I've seen with the SLC6A4 effect being discussed in this article. Some of it was probably completely unintentional, and some of it probably amounts to conscious p-hacking and fishing.
The worst part about this, that's hard to convey, is that, yes, science probably works itself out eventually most of the time? But there's such a focus on popularity and prestige, fads, regardless of veridicality, and much less so on boring rigor and correctness, that entire careers can be made or broken on complete nonsense. The person who catapaults a completely empty finding to fad status has a career elevated permanently, to a nice named-chair-full professor position. The person who is trying to be rigorous, maybe even dispute the finding or disprove it? Much less clear what happens to them, and often it's a thankless task. That is, the fad makes a career, and even after the fad is discredited, people shrug and say "oh well, that person just happened to have a good idea that was wrong." The people who do the hard work of replicating it, disproving it? Well, that's not interesting or worthwhile to reward.
Academics is really broken, at least in many fields.
This shows why you have to explicitly quantify statistical power and error model for your study design. Tools are good, but not good enough to do it for you.
Then, do calculate effect size instead of a binary answer. You will get answer with a digit in fifth place with a proper size unbiased sample. Note that for proper genome study, the sample size for observational studies is in tend of thousands barring inbred model studies. (Mice or men, with easily detectable disease process.)