In article [EMAIL PROTECTED],
Robert J. MacG. Dawson [EMAIL PROTECTED] wrote:
"W. D. Allen Sr." wrote:
A common mistake made in statistical inference is to assume every data set
is normally distributed. This seems to be the rule rather than the
exception, even among professional
Re assumptions for inference...
- Forwarded message from Robert J. MacG. Dawson -
What is needed in the small-sample case is outside _knowledge_ (not
"well, it _might_ be true" or "in this discipline we usually assume..."
assumptions!) about the distribution - without this we should not
W. D. Allen Sr. [EMAIL PROTECTED] wrote in message
nH9u6.6370$[EMAIL PROTECTED]">news:nH9u6.6370$[EMAIL PROTECTED]...
A common mistake made in statistical inference is to assume every data set
is normally distributed. This seems to be the rule rather than the
exception, even among
The second sentence here ensures that generalisability to a population
IS an issue for statistics. And a big issue, usually overlooked.
For that matter, many applications of statistics do use sampling, not
random assignment (market surveys, for example) and in these
applications Dennis'
given random assignment the generalizability of results to a population is
not an issue for statistics. It's a question of what a plausible
population is, given the procedure for obtaining subjects
On Thu, 22 Mar 2001, dennis roberts wrote:
using and interpreting inference procedures under
here is my entry for the most common mistake made in statistical inference ...
using and interpreting inference procedures under the assumption of SRS
simple random samples ... when they just can't be
this permeates across almost every technique ... and invades almost every
study ever
W. D. Allen Sr. [EMAIL PROTECTED] wrote:
: Either the Chi Square or S-K test, as appropriate, should be conducted to
: determine normality before interpreting population percentages using
: standard deviations.
I don't understand why one would want to use the normal distribution for
