Dear all,
I am interested in what others are doing when faced with techniques that
appear in standard textbooks that are "simpler" (either computationally
and/or conceptually) than better (but more difficult) techniques. My
concern is when the "superior" techniques is either inaccessible to the
audience (for instance, a "stat" 1011 class) or would take considerably
longer to teach (and the semester isn't long enough now) or requires use
of the computer for almost any sample. Some examples of techniques that I
see in lots of stat textbooks but would rarely be used by a
statistician are: 1) chi-square goodness of fit to test for normality
(when Shapiro-Wilk is much better for the univariate case and the
Henze-Zirkler for the multivariate case); 2) paired sample t-tests
(usually better options here such as ANCOVA); 3) sign test (randomization
tests are much superior). I'm sure I left out/didn't think of plenty of
other cases. My question to the group, as someone at the beginning of a
career teaching statistics, is what to do? Should some of these tests be
left out (knowing the students may run into the tests in future coursework
or in some research? Should the better procedures always be taught,
knowing that the additional difficulty due to level of
mathematics/concepts/computational load may well lose many students? I
don't know yet; What do you thing?
___________________________________
Christopher Mecklin
Doctoral Student, Department of Applied Statistics
University of Northern Colorado
Greeley, CO 80631
(970) 304-1352 or (970) 351-1684
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