Muriel Strand writes:
>i gather that a collection of events which is analyzed with statistics
>must have sufficient similarity (between each event) for the analysis to
>be accurate/precise. how similar is sufficient? can anyone recommend
>refs (preferably books) that discuss this issue, and provide guidelines
>for assuring sufficient similarity? does this consideration affect the
>appropriate choice of model?
I'm not sure what you mean by "accurate/precise", but you will often see
excellent analyses done on very diverse populations. For example, a random
sample of people in California will have quite a mix of people. You can get
very precise estimates of things like income level and unemployment
percentages for all Californians, in spite of the huge difference between
residents of Los Angeles County compared to residents of Orange County.
In clinical trials, there is often a tension between defining the study
population narrowly and defining it broadly. A narrow population (e.g.,
excluding elderly patients or patients with co-morbid conditions) can reduce
variation and make it easy to discover trends and patterns. But such a
narrow population is often difficult to generalize from. Most doctors don't
have the luxury of excluding old patients or patients who are sick from
several conditions simultaneously.
If you want a good guideline, you need to consult subject matter experts and
not statisticians. For example, only a doctor could tell you the trade-offs
between defining the population of asthmatic children broadly or narrowly.
Steve Simon, [EMAIL PROTECTED], Standard Disclaimer.
STATS - Steve's Attempt to Teach Statistics: http://www.cmh.edu/stats
