5 ms·

Could it also be because Statistics is hard and that a lot of researchers don't have a good grasp of it?

by nkassis 17y ago

marshallp 17y ago
Especially these bio/medical types, rigorous thinking isn't a requirement in their qualification process.
- applicative 17y ago
  Isn't statistics what you resort to when there isn't scope for rigorous thinking?
  - marshallp 17y ago
    No, statistics requires rigorous thought to be applied properly. That's why it's usually taught in the mathematics department.
    - tokenadult 17y ago
      Teaching a first course in statistics in the mathematics department (rather than in a dedicated department of statistics) may be a mistake. http://statland.org/MAAFIXED.PDF http://statland.org/MAAFIXED.PDF http://escholarship.org/uc/item/6hb3k0nz http://escholarship.org/uc/item/6hb3k0nz
  - roundsquare 17y ago
    It can be if its used incorrectly. This is why they said reviewers needed to hold studies to a minimal standard of biological plausibility There's two "good" ways to do this (as far as I can see). 1) Come up with a biologically plausible idea and test it, using statistics to look at results. 2) Find a pattern in the statistics and find a biologically plausible explanation. The biology alone isn't enough, you need to statistics to back up and show actual results. However, using statistics alone and in the way indicated in the article (looking at every test and every subgroup, etc...) is exactly what your saying: avoiding rigorous thinking in favor of getting a result.
Eliezer 17y ago
There are plenty of methods (cough report Bayesian likelihood ratios cough and publish your raw data cough) that are simple enough for even average scientists to use. They would rather use much more complicated statistics, though, because then they get to publish more papers with "significant" results. If you can handle calculus, which most scientists take, then you can handle likelihood ratios, believe me. But the incentives are terrible, which is quite a different thing from supposing that the average PhD is too dumb to learn good statistics if the incentives were strong.

Could it also be because Statistics is hard and that a lot of researchers don't have a good grasp of it?

by nkassis 17y ago

Could it also be because Statistics is hard and that a lot of researchers don't have a good grasp of it?

marshallp 17y ago

Especially these bio/medical types, rigorous thinking isn't a requirement in their qualification process.

applicative 17y ago
Isn't statistics what you resort to when there isn't scope for rigorous thinking?
- marshallp 17y ago
  No, statistics requires rigorous thought to be applied properly. That's why it's usually taught in the mathematics department.
  - tokenadult 17y ago
    Teaching a first course in statistics in the mathematics department (rather than in a dedicated department of statistics) may be a mistake. http://statland.org/MAAFIXED.PDF http://statland.org/MAAFIXED.PDF http://escholarship.org/uc/item/6hb3k0nz http://escholarship.org/uc/item/6hb3k0nz
- roundsquare 17y ago
  It can be if its used incorrectly. This is why they said reviewers needed to hold studies to a minimal standard of biological plausibility There's two "good" ways to do this (as far as I can see). 1) Come up with a biologically plausible idea and test it, using statistics to look at results. 2) Find a pattern in the statistics and find a biologically plausible explanation. The biology alone isn't enough, you need to statistics to back up and show actual results. However, using statistics alone and in the way indicated in the article (looking at every test and every subgroup, etc...) is exactly what your saying: avoiding rigorous thinking in favor of getting a result.

Eliezer 17y ago

There are plenty of methods (cough report Bayesian likelihood ratios cough and publish your raw data cough) that are simple enough for even average scientists to use. They would rather use much more complicated statistics, though, because then they get to publish more papers with "significant" results. If you can handle calculus, which most scientists take, then you can handle likelihood ratios, believe me. But the incentives are terrible, which is quite a different thing from supposing that the average PhD is too dumb to learn good statistics if the incentives were strong.