Some psychologists argue strongly for bayesian stats

Only last week, we heard scathing (and well-argued) remarks against the use of Bayesian methods for scientific hypothesis testing. Yet, some very smart people argue in precisely the opposite direction…

Required:

Kruschke, J. K. (2011). Bayesian Assessment of Null Values Via Parameter Estimation and Model Comparison. Perspectives on Psychological Science, 6(3), 299–312. doi:10.1177/1745691611406925

Recommended:

Dienes, Z. (2011). Bayesian Versus Orthodox Statistics: Which Side Are You On? Perspectives on Psychological Science, 6(3), 274–290. doi:10.1177/1745691611406920

Supplemental:

Kruschke, John K. (2013). Bayesian estimation supersedes the t test. Journal of Experimental Psychology: General, 142(2), 573–603. doi:10.1037/a0029146

Despite the appeal of Dienes’ preferred method of model choice, given it’s analogous structure to Neyman-Pearson Null Hypothesis Significance Testing (NHST), the results appear to vary quite readily given your choice of prior. Of course, Deines – and some other Bayesians – think this is a strength.

However, it just feels safer to use Kruschke’s preferred method of the construction of “credible intervals” – much more analogous to frequentist confidence intervals. Of course, maybe confidence intervals are superior to NHST in the frequentist world as well…