5 ms·

I feel like we need an opposite saying for sentiments like this to the age old "correlation does not equal causation", something like "correlation does not mean

by voxl 6y ago

I feel like we need an opposite saying for sentiments like this to the age old "correlation does not equal causation", something like "correlation does not mean automatically dismiss"

Quekid5 6y ago
Indeed, and this finding could at the very least be said to have some plausible underlying (perhaps indirect) causation.
graderjs 6y ago
Correlation doesn't imply causation but it could co-occur with it Too long Correlative causation is more correlated with causation than non-correlative causation Too tongue twisting Correlation is closer to causation than no correlation I like that Ignoring possible causation is correlated with stupidity Too cruel Correlation does not equal causation but they are correlated Not bad Anyone else?
- gvx 6y ago
  I always liked xkcd's "Correlation doesn't imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing 'look over there'." (https://xkcd.com/552/ https://xkcd.com/552/) although it is rather long.
- endgame 6y ago
  Correlation correlates with causation.
- alberto_ol 6y ago
  Correlation doesn't imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing 'look over there'. https://xkcd.com/552/ https://xkcd.com/552/
kkoncevicius 6y ago
The comment you replied to has nothing to do with "correlation does not equal causation". The point was that fishing for results, by trying everything, is guaranteed to find something. So if, in a study like this, they looked at differences in morality rate between: 1) men and women, 2) younger and older people, 3) younger and older surgeons, 4) surgeries in summer vs winter 4) surgeries in morning vs evening 5) etc etc.... And it came down all the way to n) surgeries on birthday - to find an effect. Then it would be almost guaranteed that such a finding is spurious.
- emn13 6y ago
  This is a huge problem in scientific papers; it's very, very common to see results for all kinds of metrics with confidence intervals or p-values and then see a few "significant" measurements, without a mention of the fact that multiple tests were made - and implicitly, possibly many more tests were made during the exploratory phase of the research. What does significance even mean then? Hard to say (there are techniques to try and compensate of course, but they have their own issues). One simple way we can at least mitigate that problem is by requiring far lower p-values (or wider CI's), and where that's not feasible, require a much clearer-eyed explanation and acceptance of the fact that such research cannot be trivially supported by statistics, and instead additionally requires careful experimental setup and consideration of causal networks. Basically: if you have p = 0.0001 or whatever I'm more willing to believe that publication biases and multiple testing aren't super likely to cause false positives that often. But without that, you want a clear hypothesis and proposed way to test published beforehand, and just one test, and ideally a clear hypothesis about causation etc too, so you can critically push and prod the results to try and distinguish noise from signal. A p=0.03 just isn't very obvious, at all. In general, I think modern science is too reliant on statistics over complex systems, and in the effort to tease out significance then needs to try and correct for all kinds of known interference (confounders) and other effects; thus then need to use more advanced statistical models and less general assumptions about distributions (whether for significance, or for mathematical tractability), that it's just very hard for anyone to say they didn't make some systematic error somewhere. And sure, being an expert in the subject matter and having an expert statistician on hand helps, but making reasoning errors is too easy; too human to reliably avoid. Instead of seeking signals in noise, we should be targeting research more narrowly to parts of the puzzle that we can measure better, then use classical plain logic to put the pieces together - not try and measure the whole thing in one go. After we put all the well-measured pieces together, validating with tricky statistics is reasonable as a sanity check, but not much more than that. If common sense is hard, statistics is harder, even for statisticians. Interpreting results like this as any more than "huh, that's something we could look into" is unwise.
yellowcake0 6y ago
The website is currently unavailable so I can't read the article. However p-hacking, to which I believe the parent comment was referring, is a separate issue. I agree that the incessant belaboring of the difference between correlation and causation in these types of threads is tiresome, but I don't think it applies in this case.
randomsearch 6y ago
How about "if you find a correlation and want to report this as if you think it's significant, then you should formulate a specific hypothesis and perform independent experiments to test it, rather than publishing junk."
- randomsearch 6y ago
  Apparently this isn't junk: I couldn't access the original at the time of posting, so I relied on a second hand bogus interpretation. Apologies!
wruza 6y ago
Do we? Wait for an hour and there will be a bunch of plausible..apparent descriptions that “invalidate” that “they’re just negligent on their birthday”.

by voxl 6y ago

I feel like we need an opposite saying for sentiments like this to the age old "correlation does not equal causation", something like "correlation does not mean automatically dismiss"

Quekid5 6y ago

Indeed, and this finding could at the very least be said to have some plausible underlying (perhaps indirect) causation.

graderjs 6y ago

Correlation doesn't imply causation but it could co-occur with it Too long Correlative causation is more correlated with causation than non-correlative causation Too tongue twisting Correlation is closer to causation than no correlation I like that Ignoring possible causation is correlated with stupidity Too cruel Correlation does not equal causation but they are correlated Not bad Anyone else?

gvx 6y ago
I always liked xkcd's "Correlation doesn't imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing 'look over there'." (https://xkcd.com/552/ https://xkcd.com/552/) although it is rather long.
endgame 6y ago
Correlation correlates with causation.
alberto_ol 6y ago
Correlation doesn't imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing 'look over there'. https://xkcd.com/552/ https://xkcd.com/552/

kkoncevicius 6y ago

The comment you replied to has nothing to do with "correlation does not equal causation". The point was that fishing for results, by trying everything, is guaranteed to find something. So if, in a study like this, they looked at differences in morality rate between: 1) men and women, 2) younger and older people, 3) younger and older surgeons, 4) surgeries in summer vs winter 4) surgeries in morning vs evening 5) etc etc.... And it came down all the way to n) surgeries on birthday - to find an effect. Then it would be almost guaranteed that such a finding is spurious.

emn13 6y ago
This is a huge problem in scientific papers; it's very, very common to see results for all kinds of metrics with confidence intervals or p-values and then see a few "significant" measurements, without a mention of the fact that multiple tests were made - and implicitly, possibly many more tests were made during the exploratory phase of the research. What does significance even mean then? Hard to say (there are techniques to try and compensate of course, but they have their own issues). One simple way we can at least mitigate that problem is by requiring far lower p-values (or wider CI's), and where that's not feasible, require a much clearer-eyed explanation and acceptance of the fact that such research cannot be trivially supported by statistics, and instead additionally requires careful experimental setup and consideration of causal networks. Basically: if you have p = 0.0001 or whatever I'm more willing to believe that publication biases and multiple testing aren't super likely to cause false positives that often. But without that, you want a clear hypothesis and proposed way to test published beforehand, and just one test, and ideally a clear hypothesis about causation etc too, so you can critically push and prod the results to try and distinguish noise from signal. A p=0.03 just isn't very obvious, at all. In general, I think modern science is too reliant on statistics over complex systems, and in the effort to tease out significance then needs to try and correct for all kinds of known interference (confounders) and other effects; thus then need to use more advanced statistical models and less general assumptions about distributions (whether for significance, or for mathematical tractability), that it's just very hard for anyone to say they didn't make some systematic error somewhere. And sure, being an expert in the subject matter and having an expert statistician on hand helps, but making reasoning errors is too easy; too human to reliably avoid. Instead of seeking signals in noise, we should be targeting research more narrowly to parts of the puzzle that we can measure better, then use classical plain logic to put the pieces together - not try and measure the whole thing in one go. After we put all the well-measured pieces together, validating with tricky statistics is reasonable as a sanity check, but not much more than that. If common sense is hard, statistics is harder, even for statisticians. Interpreting results like this as any more than "huh, that's something we could look into" is unwise.

yellowcake0 6y ago

The website is currently unavailable so I can't read the article. However p-hacking, to which I believe the parent comment was referring, is a separate issue. I agree that the incessant belaboring of the difference between correlation and causation in these types of threads is tiresome, but I don't think it applies in this case.

randomsearch 6y ago

How about "if you find a correlation and want to report this as if you think it's significant, then you should formulate a specific hypothesis and perform independent experiments to test it, rather than publishing junk."

randomsearch 6y ago
Apparently this isn't junk: I couldn't access the original at the time of posting, so I relied on a second hand bogus interpretation. Apologies!

wruza 6y ago

Do we? Wait for an hour and there will be a bunch of plausible..apparent descriptions that “invalidate” that “they’re just negligent on their birthday”.