Effect size is significantly more important than statistical significance
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Re: Effect size is significantly more important than statistical significance
#2Speaking not to this study in particular necessarily, I strongly agree with the general point. Science has really been held back by an over-focusing on "significance". But I'm not really interested in a pile of hundreds of thousands of studies that establish a tiny effect with suspiciously-just-barely-significant results. I'm interested in studies that reveal robust results that are reliable enough to be built on to produce other results. Results of 3% variations with p=0.046 aren't. They're dead ends, because you can't put very many of those into the foundations of future papers before the probability of one of your foundations being incorrect is too large.
To the extent that those are hard to come by... Yeah! They are! Science is hard. Nobody promised this would be easy. Science shouldn't be something where labs are cranking out easy 3%/p=0.046 papers all the time just to keep funding. It's just a waste of money and time of our smartest people. It should be harder than it is now.
Too many proposals are obviously only going to be capable of turning up that result (insufficient statistical power is often obvious right in the proposal, if you take the time to work the math). I'd rather see more wood behind fewer arrows, and see fewer proposals chasing much more statistical power, than the chaff of garbage we get now.
If I were King of Science, or at least, editor of a prestigious journal, I'd want to put word out that I'm looking for papers with at least one of some sort of significant effect, or a p value of something like p = 0.0001. Yeah. That's a high bar. I know. That's the point.
"But jerf, isn't it still valuable to map out all the little things like that?" No, it really isn't. We already have every reason in the world to believe the world is drenched in 1%/p=0.05 effects. "Everything's correlated to everything", so that's not some sort of amazing find, it's the totally expected output of living in our reality. Really, this sort of stuff is still just below the noise floor. Plus, the idea that we can remove such small, noisy confounding factors is just silly. We need to look for the things that stand out from that noise floor, not spending billions of dollars doing the equivalent of listening to our spirit guides communicate to us over white noise from the radio.
Re: Effect size is significantly more important than statistical significance
#3Speaking not to this study in particular necessarily, I strongly agree with the general point. Science has really been held back by an over-focusing on "significance". But I'm not really interested in a pile of hundreds of thousands of studies that establish a tiny effect with suspiciously-just-barely-significant results. I'm interested in studies that reveal robust results that are reliable enough to be built on to…
I blame most of this on pop science. It's absolutely ruined the average public's respect for the behind the scenes work doing interesting stuff in every field. What's worse is the attitude it breeds. Anti-intellectualism runs rampant amongst even well educated members of my social circle. It's frustrating to say the least.
Re: Effect size is significantly more important than statistical significance
#4Speaking not to this study in particular necessarily, I strongly agree with the general point. Science has really been held back by an over-focusing on "significance". But I'm not really interested in a pile of hundreds of thousands of studies that establish a tiny effect with suspiciously-just-barely-significant results. I'm interested in studies that reveal robust results that are reliable enough to be built on to…
> Plus, the idea that we can remove such small, noisy confounding factors is just silly. We need to look for the things that stand out from that noise floor
We have found most of them, and all the easy ones. Today the interesting things are near the noise floor. 3000 years ago atoms were well below the noise floor, now we know a lot about them - most of it seems useless in daily life yet a large part of the things we use daily depend on our knowledge of the atom.
Science needs to keep separating things from the noise floor. Some of them become important once we understand it.
Re: Effect size is significantly more important than statistical significance
#5Speaking not to this study in particular necessarily, I strongly agree with the general point. Science has really been held back by an over-focusing on "significance". But I'm not really interested in a pile of hundreds of thousands of studies that establish a tiny effect with suspiciously-just-barely-significant results. I'm interested in studies that reveal robust results that are reliable enough to be built on to…
A few years ago, HN comments complained about the censorship that only leaves successful studies. We need to report on everything we've tried, so we don't walk around on donuts.
What's missing in my mind is admitting that results were negative. I'm reading up on financial literacy, and many studies end with some metrics being "great" at p 5%, but then some other metrics are also "great" at p 10%, without the author ever explaining what they would have classified as bad. They're just reported without explanation of what significance they would expect (in their field).
Re: Effect size is significantly more important than statistical significance
#6Speaking not to this study in particular necessarily, I strongly agree with the general point. Science has really been held back by an over-focusing on "significance". But I'm not really interested in a pile of hundreds of thousands of studies that establish a tiny effect with suspiciously-just-barely-significant results. I'm interested in studies that reveal robust results that are reliable enough to be built on to…
This is clearly a cost/benefit tradeoff, and the sweet spot will depend entirely on the field. If you are studying the behavior of heads of state, getting an additional N is extremely costly, and having a p=0.05 study is maybe more valuable than having no published study at all, because the stakes are very high and even a 1% chance of (for example) preventing nuclear war is worth a lot. On the other hand, if you are studying fruit flies, an additional N may be much cheaper, and the benefit of yet another low effect size study may be small, so I could see a good argument being made for more stringent standards. In fact I know that in particle physics the bar for discovery is much higher than p=0.05.
Re: Effect size is significantly more important than statistical significance
#7Speaking not to this study in particular necessarily, I strongly agree with the general point. Science has really been held back by an over-focusing on "significance". But I'm not really interested in a pile of hundreds of thousands of studies that establish a tiny effect with suspiciously-just-barely-significant results. I'm interested in studies that reveal robust results that are reliable enough to be built on to…
The problem is that when you’re on the cusp of a new thing, unless you’re super lucky, the result will necessarily be near the noise floor. Real science is like that.
But I definitely agree it’d be nice to go back and show something is true to p=.0001 or whatever. Overwhelmingly solid evidence is truly a wonderful thing, and as you say, it’s really the only way to build a solid foundation.
When you engineer stuff, it needs to work 99.99-99.999% of the time or more. Otherwise you’re severely limited to how far your machine can go (in terms of complexity, levels of abstraction and organization) before it spends most of its time in a broken state.
I’ve been thinking about this while playing Factorio: so much of our discussion and mental modeling of automation works under the assumption of perfect reliability. If you had SLIGHTLY below 100% reliability in Factorio, the game would be a terrible grind limited to small factories. Likewise with mathematical proofs or computer transistors or self driving cars or any other kind of automation. The reliability needs to be insanely good. You need to add a bunch of nines to whatever you’re making.
A counterpoint to this is when you’re in an emergency and inaction means people die. In that case, you need to accept some uncertainty early on.
Re: Effect size is significantly more important than statistical significance
#8Speaking not to this study in particular necessarily, I strongly agree with the general point. Science has really been held back by an over-focusing on "significance". But I'm not really interested in a pile of hundreds of thousands of studies that establish a tiny effect with suspiciously-just-barely-significant results. I'm interested in studies that reveal robust results that are reliable enough to be built on to…
Not only is it not valuable to publish tons of studies with p=.04999 and small effect size, in fact it's harmful. With so many questionable results published in supposedly reputable places it becomes possible to "prove" all sorts of crackpot theories by selectively citing real research. And if you try to dispute the studies you can get accused of being anti-science.
Re: Effect size is significantly more important than statistical significance
#9I wonder if we should separate the roles of scientist and researcher. Universities would have generalist "scientists" who's job would be to consult for domain-specialized researchers to ensure they're doing the science and statistics correctly. That way, we don't need every researcher in every field to have a deep understanding of statistics, which they often don't.
Either that or stop rewarding such bad behavior. Science jobs are highly competitive, so why not exclude people with weak statistics? Maybe because weak statistics leads to more spurious exciting publications which makes the researcher and institution look better?
Re: Effect size is significantly more important than statistical significance
#10Speaking not to this study in particular necessarily, I strongly agree with the general point. Science has really been held back by an over-focusing on "significance". But I'm not really interested in a pile of hundreds of thousands of studies that establish a tiny effect with suspiciously-just-barely-significant results. I'm interested in studies that reveal robust results that are reliable enough to be built on to…
I agree we shouldn't listen to noise, but small effect size is not necessarily noise. (I agree it is highly correlated.) I mean, QED's effect size on g factor is 1.001. QED was very much worth finding out.