I am new to the list so I am jumping into the middle of this. However, we
have to start teaching hypothesis testing somewhere. Even if it goes the
way of the Edsel, it will be a slow death because many of us will continue
to use when we feel it is appropriate to the question. However, I tell my
students that there are always more complicated ways to do things. In many
instances these take more math ability, computer skills, or time than I
have to explain them. So I am going to show them a method that will work
although it won't necessarily be the most powerful or efficient. The key is
to first understand inference thoroughly before jumping into more
complicated things. You don't teach a first grader multiplication before
they understand addition and I don't teach a nursing student a logistic
regression before they can understand a chi-square goodness of fit. If we
had the ability to teach all the students what we thought they needed to
know, we might do things a little differently. Someone mentioned Joe Ward.
His colleague Earl Jennings once told me that the biggest impediment to
understanding linear models was learning the t test, anova, and regression
techniques separately. When I teach linear models I try to get the students
to unlearn a lot of what they know. It seems a waste of time but we are not
always in charge of the curriculum. When you are given 3 hours to teach a
student something about statistics, do you start with linear models?
Probably not. Well, I did not intend this to be quite so long so I'll shut up.
At 06:05 PM 4/7/00 -0600, you wrote:
>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 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 course work
>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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>
Paul R. Swank, PhD.
Advanced Quantitative Methodologist
UT-Houston School of Nursing
Center for Nursing Research
Phone (713)500-2031
Fax (713) 500-2033
===
This list is open to everyone. Occasionally, less thoughtful
people send inappropriate messages. Please DO NOT COMPLAIN TO
THE POSTMASTER about these messages because the postmaster has no
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termination of the list.
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problem of inappropriate messages and information about how to
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