🚀 go-pugleaf

awhichel@robin.rp.csiro.au (Adrian Whichello) writes:

>I have just finished teaching an Introductory Statistics course (to electrical
>engineers) and I'm sure I put more of them to sleep than I actually taught. As 
>this is a common complaint about Statistics courses in general, does anyone 
>have any suggestions as to how statistics can be taught in a stimulating
>fashion? I tried to use examples and anecdotes from my own background in
>industry wherever possible, which of course helped, but I'm sure you know the
>general problem. Any suggestions? 
>							Adrian Whichello

I've taught Intro Prob/Stats to Computer Science students a few times. 

There are a couple of points I've found that make the teaching and learning
more effective. 

Firstly, I thinks it's too easy to presume that Prob and Stats is easy to
learn. I'd venture that many practicing _scientists_ who use stats (rather
than statisticians) really only learn their methods when they have to use 
them - their u/grad classes were perfunctory exercises and prediction of
exam questions. What I do is to indicate to students what is HARD about the
concepts, and what is HARD about the methods they are using, so they won't
feel too bad about wrong answers, and will get some achievement from reasoning
correctly.

Secondly, there are a lot of issues which intro stats texts gloss over. For
example: "What is data"? [if it's not a sample]; "What is estimation"? [if
it's not about exploiting samples]; "How do you get data"?; "What are
measures of likelihood telling us"?; "Assumptions"?????, etc. I find it
useful to attack stats problems (in teaching) with a "what you're given"
and "what you want" approach. The connection between these two is problem
solving behaviour which uses the "valuable words" of the course.

Thirdly - anecdotes and historical examples of _things_gone_wrong_ (the
better approach will be discussed NEXT week). I think it's a teaching
mistake to include too many paradoxes and tricky situations, but a few
reveal how powerful the concepts and methods are.

Lastly, to improve their basic skills, the students get more confidence
in problem solving if they can identify the problem _type_ as a matter
of routine. The _big_ deal was learning the concepts and methods the
_first_ few time they applied them. The routine stage of skill acquisition
needs practice at problem identification AND at physical things like 
table look-up and the basic pattern of arguments and expressions.

Tomasso.

PS: This year, it was (I think) fun to use Miranda as a student-devised
implementation of a data/stats processing program. All the formulas in
class were easy to implement in a functional language (even to the extent
of data-in, table-out), and testing was a cinch.

In article <28OCT199218553493@robin.rp.csiro.au>, awhichel@robin.rp.csiro.au (Adrian Whichello) writes:
> I have just finished teaching an Introductory Statistics course (to electrical
> engineers) and I'm sure I put more of them to sleep than I actually taught. As 
> this is a common complaint about Statistics courses in general, does anyone 
> have any suggestions as to how statistics can be taught in a stimulating
> fashion? I tried to use examples and anecdotes from my own background in
> industry wherever possible, which of course helped, but I'm sure you know the
> general problem. Any suggestions? 


You might post these questions on bit.listserv.edstat-l .