On Tue, 18 May 2004 11:30:49 -0400, Jackie Patti <[email hidden]>
Quoted message said:Quoted message said:Quoted message said:Results: By 1 year, mean (±SD) weight change for
persons on the low-carbohydrate diet was –5.1 ± 8.7
kg compared with –3.1 ± 8.4 kg for persons on the
conventional diet. Differences between groups were not
significant (–1.9 kg [95% CI, –4.9 to 1.0 kg]; P =
0.20).
Quoted message said:p = 0.20 is a description of the measurable significance,
and this is a very low measure, comparable to just one
widget's weight being off in our example above. It means
they consider this to possibly be just luck and can't know
if it's real for the population without more study.
Quoted message said:Generally, a scientist wants p > 0.90 or > 0.95 or > 0.99.
Which cut-off is acceptable depends on the specific type
of study.But p = 0.2 *definetly* is not good enough to say the
differences between those groups is true (for that
particular measurement, other measurments in the study are
significant).
Again, the difference between the scientific definition and
how the man-in-the-street reacts. If your p=.2 means that
there is a 20% chance that the result was arrived at by
chance, OK, the man-in-the-street may be willing to agree
that the matter has not been PROVEN - but he'll probably be
willing to bet with the 80% chance that it WASN'T by chance.
Quoted message said:So in summary, they are saying they measured a difference,
but the difference isn't meaningful enough to signify
anything beyond what happened to their sample.
They're saying that they haven't PROVED that the difference
in results was caused by the difference in diet under study.
Nonetheless, they are also saying that the odds are 4-1 that
it WAS, in fact, the lowcarb diet that made the difference.
Many people are willing to invest some resources (time,
effort, money, etc.) if the odds are 4-1 in their favor.