Articles of Interest
 
Bear, G.(1995). Computationally intensive methods warrant reconsideration
of pedagogy in statistics. Behav. Research Methods, Instruments, and Computers, 27(2), 144-147. 
 
Abstract: Computationally intensive methods of statistical inference do
not fit the current canon of pedagogy in statistics. To accommodate these
methods and the logic underlying them, I propose seven pedagogical principles:
(1) Define inferential statistics as techniques for reckoning with chance. 
(2) Distinguish three types of research:
· sample surveys, in which statistics affords generalization from the cases studied;
· experiments, in which statistics detects systematic differences among the batches of data obtained in the several conditions;
· correlational studies, in which statistics detects systematic associations between variables.
(3) Teach random-sampling theory in the context of sample surveys, augmenting the conventional treatment with bootstrapping.
Regarding experimentation,
(4) note that random assignment fosters internal but not external validity, 
(5) explain the general logic for testing a null model, and 
(6) teach randomization tests as well as t, F, and chi2. 
Regarding correlational studies, (7) acknowledge the problems of applying
inferential statistics in the absence of deliberately introduced randomness.