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In my experience, college freshmen are already familiar with most of
descriptive statistics (central tendency, frequency distributions,
scatterplots) by the time they reach college, so I only mention these
in brief. I can quickly explain standard deviation as something like
an "average" distance from the mean, and the correlation correficient
is understandable using corresponding plots. As for inferential
statistics, I think this is too important a topic to completely skip.
The basic idea isn't so tough and I present it in about 5 minutes. I
start with class data on frequency of crying (large male-female
difference here). I describe the two interpretations for our gender
difference (male and female college students differ in crying frequency
vs. they don't and our class data represent chance sampling error).
Then I point out that we scientists are conservative and unwilling to
say our sample data represent a "real" difference unless we can rule
out the chance explanation. I tell them that in intro statistics, they
will learn ways to calculate the likelihood that the chance explanation
is correct (and if it turns out to be quite unlikely, say <p.05,
then we scrap the chance explanation and announce our findings). Five minutes is enough. They don't need to know about calculations, but a general understanding of the logic in inferential statistics should help them later. It also coincides nicely with our emphasis on critical thinking in dealing with research findings. --Dave [EMAIL PROTECTED] wrote:
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- Question about stats in intro texts David Campbell
- Question about stats in intro texts Drnanjo
- Re: Question about stats in intro texts Karl L. Wuensch
- Re: Question about stats in intro texts David Campbell
