A common divide-and-conquer approach for Bayesian computation with big data is to partition the data, perform local inference for each piece separately, and combine the results to obtain a global posterior approximation. While being conceptually and computationally appealing, this method involves the problematic need to also split the prior for…

The authors provide generalizations of R. J. Boik's (1993) studentized maximum root (SMR) procedure that allow for simultaneous inference on families of product contrasts including simple effect contrasts and differences among simple effect contrasts in coherent analyses of data from 2-factor fixed-effects designs. Unlike the F-based simultaneous…

Investigates student interpretation of research by asking samples of undergraduates, graduates, and faculty questions concerning the implications of random sampling and random assignment. Finds that random sampling is understood while the role of random assignment in interpretation of results is misunderstood. Concludes there is a need for…