| Hand in WED
Table C: p.341, 6.48 CEO pay again (what you would do if you didn't have Table A) p. 341, *6.46, 6.49 general z statistic, significance,Turn the page--6.49 continues. p. 342 *6.50 patent protection; another z. = = = = = = = = = = Fixed significance levels: if you only have table C, what can you say? p. 337, 6.37 testing number generator 6.38 nicotine content = = = = = = = = = = *p. 342, 6.52 1% vs 5% * 6.53 define stat. signif. *p. 343, 6.54 knife edge .05 *p. 345, 6.55 and 56 effect of n = = = = = = = = = = Review of ch. 6--these review material that may be on Exam 3: p. 339 6.40 job satisfaction, 2 sided p. 360 6.74 wine--stemplot, CI , test. Notice "less sensitive" noses will have higher thresholds. p. 362, 6.79 a,b effect of sample size 6.83 Train Welfare mothers This kind of study was the basis (plus conservative philosophy) for our present "welfare reform." = = = = = = = = = = = = = = = = = Sec. 6.3, (pp. 344-48 is new), and above notes p. 346 6.57 test ok? p.348 6.61 strong vs. signif. Postpone this one:. 347 6.58 500 tests for psychic powers p.3486.59 what is significance good for? 6.60 radar detectors Postpone these: A. You have a theory that walls painted pale pink will have a mellowing effect on elementary school students and produce better grades. So you receive permission to repaint one classroom from each grade at the local school over Christmas vacation (the others stay as they were). Indeed, the students in the pink classrooms do better on end-of-year tests. What criticism can be made of your experiment, and how could it have been designed to avoid this? 6.62 77 potential schizophrenia markers |
Optional
Review: p. 360, 6.75
|
Questions on HW:
"significance ", table C, see Day
33
= = = = = = = = = = = = = = = = = = = = = =
"Significance
testing" vs. "Hypothesis testing"--gathering evidence vs.
making
decisions.
Sec 6.3, cont'd: cautions and
limitations: pp. 345-348
>>Data must be from SRS or reasonable
facsimile
All the other
warnings
p.
312: normality, watch out for outliers, skewness. Sigma known
or n large.
>>(not in text) You cannot legitimately test a hypothesis on the same data that first suggested that hypothesis. Every data set will turn up with some unusual pattern if you examine it hard enough. (If you must explore and confirm with the same data set, one way is to (randomly) take half the data set, explore and generate hypotheses; then use the other half for confirmatory tests. You can use P-value to describe unusualness, but be wary of making decisions with it if you didn't expect that particular unusualness.)
All the warnings about designing experiments and
surveys still apply.
Test 3 material ends here.
Start here Wednesday
>> (not in text) Another
common lurking variable is the Hawthorne effect: People tend
to respond positively when their environment is changed in a way they know
is supposed to be "better," especially if they know they're being studied.
(Get half-page handout.)
(Prospective teachers, keep this in mind as the fads blow in and out.)
>>Multiple Tests: beware! pp.
346-7
If you do 100
tests and use the alpha = .05 significance level for each, then
the structure of testing requires this:
When all 100 null
hypotheses H0 are true, out of your 100, about 5 of the
100 (.05) will give "significant" results by chance alone (falsely
indicating the alternative hypothesis is to be preferred.)
Moral: if you use the
testing mechanism as a screening instrument for many questions,
a proportion will give falsely significant results. You can't
accept the results from such multiple tests as good evidence, only as indicating
questions requiring further, more specific study. The game gives you one
shot, not a hundred.
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