Hmmm. Personally, I think the AIC/SIC stuff is a hack.
I don’t think statistics works in the N=76 regime. In Bayesian terms, the data is sufficient to justify only a minor update, so whatever conclusions you draw will be dominated by your choice of prior. It’s interesting that it might be used in court, because it means that the lawyers will effectively be arguing about the justifications of statistical inference—or, if they are both Bayesians, how to choose a prior.
I don’t think statistics works in the N=76 regime. In Bayesian terms, the data is sufficient to justify only a minor update, so whatever conclusions you draw will be dominated by your choice of prior.
I don’t understand why you call that statistics “not working.” Do you mean frequentist statistics?
Also, that’s just flat out wrong. Often in a Bayesian analysis with a sample size of n=75 or so (or smaller!), you’ll draw the same conclusions for any reasonable choice of prior, including diffuse priors which primarily reflect the data. Choosing a different reasonable model still might result in different conclusions, so if you’re using the word ‘prior’ to include the data model, then I don’t disagree.
Choosing a different reasonable model still might result in different conclusions, so if you’re using the word ‘prior’ to include the data model, then I don’t disagree.
Right, so the word “reasonable” is prominent here, and implies some kind of subjective evaluation. Different people may very well have different notions of what constitutes a reasonable model. If we were arguing different sides of the case in court, I could just claim your model was unreasonable and determined according to your subjective preferences.
There are a lot of metrics of how good a model is (i.e. adjusted R-squared, AIC, SIC).
Edit to add: Backcasting would be a good measure.
76 data points, after adjustments.
Hmmm. Personally, I think the AIC/SIC stuff is a hack.
I don’t think statistics works in the N=76 regime. In Bayesian terms, the data is sufficient to justify only a minor update, so whatever conclusions you draw will be dominated by your choice of prior. It’s interesting that it might be used in court, because it means that the lawyers will effectively be arguing about the justifications of statistical inference—or, if they are both Bayesians, how to choose a prior.
I can’t go into specifics, but the alternatives are far worse.
I don’t understand why you call that statistics “not working.” Do you mean frequentist statistics?
Also, that’s just flat out wrong. Often in a Bayesian analysis with a sample size of n=75 or so (or smaller!), you’ll draw the same conclusions for any reasonable choice of prior, including diffuse priors which primarily reflect the data. Choosing a different reasonable model still might result in different conclusions, so if you’re using the word ‘prior’ to include the data model, then I don’t disagree.
Right, so the word “reasonable” is prominent here, and implies some kind of subjective evaluation. Different people may very well have different notions of what constitutes a reasonable model. If we were arguing different sides of the case in court, I could just claim your model was unreasonable and determined according to your subjective preferences.
I’m not sure what your point is.