4 ms·

"Is using mathy concepts to dress up poor reasoning worse than not using anything to back up your reasoning?" I believe so. If your belief is baseless, or base

by astine 11y ago

"Is using mathy concepts to dress up poor reasoning worse than not using anything to back up your reasoning?" I believe so. If your belief is baseless, or based on flimsy evidence or simple bias, it's best if that's obvious. Dressing up weak reasoning to seem stronger is a form of lying. It's what we call sophistry. A big part of the problem is that for a lot of people don't understand the math well enough to point out what's wrong with it or have a bias towards explanations that seem complex or sophisticated but really aren't. It's true that sometimes we have to make a decision based on poor or no evidence but it should be clear that that is the case when that is the case. Dressing up the argument only obfuscates that.

TeMPOraL 11y ago
Honesty is an ultimate issue here. If my reasoning is shoddy, but I plug it into some math apparatus, then it'll likely make my problems obviously wrong. If my reasoning is very inaccurate and the data uncertain, being precise about it can at least make the results salvageable. Scott Alexander argues for this position quite well in [0]. Humans can lie with statistics well. But they can lie with plain language even better. [0] - http://slatestarcodex.com/2013/05/02/if-its-worth-doing-its-worth-doing-with-made-up-statistics/ http://slatestarcodex.com/2013/05/02/if-its-worth-doing-its-...
- astine 11y ago
  "If my reasoning is shoddy, but I plug it into some math apparatus, then it'll likely make my problems obviously wrong." That's pretty clearly untrue. I remember reading a study recently where the p value was less than .01 or something like that but where the experimental design was clearly flawed. The correlation wasn't the correlation they thought they had. But because the math looked good and it was easier than actually reviewing the experiment, it was tempting to take the study on face value. I've read Scott's essay before and I understand his argument, but I don't think it works. While, you might be able to avoid some bad reasoning simply by being more systematic, you can also strengthen bad arguments with a faulty application of statistics. What Scott doesn't do is provide an analysis of how often each of these things happens. I'd argue that for each time a quick application of statistics save someone from a bad intuitive judgment, a misapplication of statistics is used to encourage a bad judgment at least one time if not more. Understand that my argument here is not that one should never use statistics or even Bayes theorem, but that a naive or lazy application can be worse than no application.
  - TeMPOraL 11y ago
    I see your point and I agree. For myself, I try to limit myself to the mathematical apparatus I feel comfortable with. I know that if I were to open a statistics textbook, I could find something to plug in my estimates and reach a conclusion, and I'm pretty sure the conclusion would be bullshit. I learned it the hard way in high school - I remember the poor results of trying to solve math and physics homework assignments on topics I didn't understand yet. The mistakes were often subtle, but devastating.

by astine 11y ago

TeMPOraL 11y ago

Honesty is an ultimate issue here. If my reasoning is shoddy, but I plug it into some math apparatus, then it'll likely make my problems obviously wrong. If my reasoning is very inaccurate and the data uncertain, being precise about it can at least make the results salvageable. Scott Alexander argues for this position quite well in [0]. Humans can lie with statistics well. But they can lie with plain language even better. [0] - http://slatestarcodex.com/2013/05/02/if-its-worth-doing-its-worth-doing-with-made-up-statistics/ http://slatestarcodex.com/2013/05/02/if-its-worth-doing-its-...

astine 11y ago
"If my reasoning is shoddy, but I plug it into some math apparatus, then it'll likely make my problems obviously wrong." That's pretty clearly untrue. I remember reading a study recently where the p value was less than .01 or something like that but where the experimental design was clearly flawed. The correlation wasn't the correlation they thought they had. But because the math looked good and it was easier than actually reviewing the experiment, it was tempting to take the study on face value. I've read Scott's essay before and I understand his argument, but I don't think it works. While, you might be able to avoid some bad reasoning simply by being more systematic, you can also strengthen bad arguments with a faulty application of statistics. What Scott doesn't do is provide an analysis of how often each of these things happens. I'd argue that for each time a quick application of statistics save someone from a bad intuitive judgment, a misapplication of statistics is used to encourage a bad judgment at least one time if not more. Understand that my argument here is not that one should never use statistics or even Bayes theorem, but that a naive or lazy application can be worse than no application.
- TeMPOraL 11y ago
  I see your point and I agree. For myself, I try to limit myself to the mathematical apparatus I feel comfortable with. I know that if I were to open a statistics textbook, I could find something to plug in my estimates and reach a conclusion, and I'm pretty sure the conclusion would be bullshit. I learned it the hard way in high school - I remember the poor results of trying to solve math and physics homework assignments on topics I didn't understand yet. The mistakes were often subtle, but devastating.