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I'd add Gelman and Hill 2007 to your reading. They addresses many of these issues, too. And I second the Kruschke BEST article.

Also keep in mind the Bayesian approach is the probability of your MODEL given the data. The data are assumed correct. By it's very function bayesian inference is a model test in an of itself, in away. Particularly if you do regression analysis. The frequentist examine the inverse-- meaning the probability of the data give you model. The model is assumed to be correct. This is something you should always keep n the back of your head while doing bayesian analysis.

Sometimes the line between the two can be blurry....

I'd add Gelman and Hill 2007 to your reading. They address many of these issues, too. And I second the Kruschke BEST article.

Also keep in mind the Bayesian approach is the probability of your MODEL given the data. The data are assumed correct. By it's very function bayesian inference is a model test in an of itself, in away. Particularly if you do regression analysis. The frequentist examine the inverse-- meaning the probability of the data give you model. The model is assumed to be correct. This is something you should always keep in the back of your head while doing bayesian analysis.

Sometimes the line between the two can be blurry....

I'd add Gelman and Hill 2007 to your reading. He addresses many of these issues, too.

Also keep in mind the Bayesian approach is the probability of your MODEL given the data. The data are assumed correct. By it's very function Bayesian inference is a model test. The Frequentist examine the inverse-- meaning the probability of the data give you model. The model is assumed to be correct. This is something you should always keep in the back of your head while doing Bayesian analysis.

Sometimes the line between the two can be blurry....

I'd add Gelman and Hill 2007 to your reading. They addresses many of these issues, too. And I second the Kruschke BEST article.

Also keep in mind the Bayesian approach is the probability of your MODEL given the data. The data are assumed correct. By it's very function bayesian inference is a model test in an of itself, in away. Particularly if you do regression analysis. The frequentist examine the inverse-- meaning the probability of the data give you model. The model is assumed to be correct. This is something you should always keep n the back of your head while doing bayesian analysis.

Sometimes the line between the two can be blurry....

I'd add Gelman and Hill 2007 to your reading. He addresses many of these issues, too.

Also keep in mid the Bayesian approach is the probability of you MODEL given the data. The data are assumed correct. By it's very function bayesian inference is a model test. The frequentist examine the inverse-- meaning the probability of the data give you model. The model is assumed to be correct. This is something you should always keep n the back of your head while doing bayesian analysis.

Sometimes the line between the two can be blurry....

I'd add Gelman and Hill 2007 to your reading. He addresses many of these issues, too.

Also keep in mind the Bayesian approach is the probability of your MODEL given the data. The data are assumed correct. By it's very function Bayesian inference is a model test. The Frequentist examine the inverse-- meaning the probability of the data give you model. The model is assumed to be correct. This is something you should always keep in the back of your head while doing Bayesian analysis.

Sometimes the line between the two can be blurry....