Still, the main post feels to me like a sales pitch...
It’s a fair point; I’m not exactly attacking the strongest representative of frequentist statistical practice. My only defense is that this actually happened, so it makes a good case study.
I assert that it is evidence in my concluding paragraph, but it’s true that I don’t give an actual argument. Whether one counts it as evidence would seem to depend on the causal assumptions one makes about the teaching and practice of statistics.
The critique of frequentist statistics, as I understand it—and I don’t think I do—is that frequentists like to count things, and trust that having large sample sizes will take care of biases for them. Therefore, a case in which frequentist statistics co-occurs with bad results counts against use of frequentist statistics, and you don’t have to worry about why the results were bad.
The whole Bayesian vs. frequentist argument seems a little silly to me. It’s like arguing that screws are better than nails. It’s true that, for any particular individual joint you wish to connect, a screw will probably connect it more securely and reversibly than a nail. That doesn’t mean there’s no use for nails.
It’s a fair point; I’m not exactly attacking the strongest representative of frequentist statistical practice. My only defense is that this actually happened, so it makes a good case study.
That’s true, and having been reminded of that, I think I may have been unduly pedantic about a fine detail at the expense of the main point.
It’s a good case study, but it’s not evidence of a problem with frequentist statistics.
I assert that it is evidence in my concluding paragraph, but it’s true that I don’t give an actual argument. Whether one counts it as evidence would seem to depend on the causal assumptions one makes about the teaching and practice of statistics.
Perhaps it’s frequentist evidence against frequentist statistics.
I think this is just a glib rejoinder, but if there’s a deeper thought there, I’d be interested to hear it.
The critique of frequentist statistics, as I understand it—and I don’t think I do—is that frequentists like to count things, and trust that having large sample sizes will take care of biases for them. Therefore, a case in which frequentist statistics co-occurs with bad results counts against use of frequentist statistics, and you don’t have to worry about why the results were bad.
The whole Bayesian vs. frequentist argument seems a little silly to me. It’s like arguing that screws are better than nails. It’s true that, for any particular individual joint you wish to connect, a screw will probably connect it more securely and reversibly than a nail. That doesn’t mean there’s no use for nails.