I'd say it would only be fraud if he misreported his methods in his papers.
Fair point, you are right. Many authors do, in fact, misreport in this fashion, but I have no evidence that John Gottman has done so.
I think the Slate author just says that she assumed his methods to have been different than they were, after reading Gladwell's story about them.
No, she doesn't just say that. This is not a difference of opinion, it's a matter of fact of what is written in the actual article. "I think" does not apply.
Also, it seems to me that if you make a model that fits the data, that doesn't at all mean that it doesn't have predictive power.
That is not what I said.
You could fit it with one data-set, and test it on another one. It just means that you now have a purely empirical model, with no built-in assumptions on why it is so.
That is a valid methodology, but not the one the article says he used. What was described in the article was pure curve-fitting to the training data, which is not predictive. You can only claim predictiveness by using the generated curve on a test set and seeing how well it does, which is exactly what the article says he did not do. You are arguing in circles!
You should, in fact, expect to fit a curve to a training set with higher accuracy than its general predictive power! You can only test its predictive power on test data.
Right, sorry, I should have been clearer that I was reacting to the article and not to you there. Or actually, I was reacting to what I thought the article said, because indeed that's not what it said! Thanks.
Fair point, you are right. Many authors do, in fact, misreport in this fashion, but I have no evidence that John Gottman has done so.
I think the Slate author just says that she assumed his methods to have been different than they were, after reading Gladwell's story about them.
No, she doesn't just say that. This is not a difference of opinion, it's a matter of fact of what is written in the actual article. "I think" does not apply.
Also, it seems to me that if you make a model that fits the data, that doesn't at all mean that it doesn't have predictive power.
That is not what I said.
You could fit it with one data-set, and test it on another one. It just means that you now have a purely empirical model, with no built-in assumptions on why it is so.
That is a valid methodology, but not the one the article says he used. What was described in the article was pure curve-fitting to the training data, which is not predictive. You can only claim predictiveness by using the generated curve on a test set and seeing how well it does, which is exactly what the article says he did not do. You are arguing in circles!
You should, in fact, expect to fit a curve to a training set with higher accuracy than its general predictive power! You can only test its predictive power on test data.