Re: Propensity

Peter Ashby wrote:

Philip <pp417@xxxxxxxxxxxxx> wrote:

I think your second example might also have something to do with the point
Peter B makes about statistical significance being arbitrary.


Up to a point. Empirically you can study disease distribution where a
common cause is known and this will give you distributions within
particular confidence issues when modelled. With that background, which
is what epidemiologists have, then modelling confounders included, you
can say that a particular cluster has no apparent common cause and even
put some numbers of the probability.

There is some information missing here. As well as determining whether a
correlation of cancer and known causes is statistically significant, any study
should also report its "power", that is what level of correlation it is expected
the statistics should be able to uncover (find statistically significant).
For example a study into the link between smoking and cancer would find no
significant correlation if it studied only 10 people for 1 year.

The study should contain a statement something like "a correlation between
increased radiation exposure and an increased cancer incidence of under 20% over
a lifetime would not be detected by this study". As well as a lower bound for
detection an upper bound for non-detection is needed, otherwise any lack of
correlation reported is meaningless.

Unfortunately this critical piece of information is rarely reported.

>>> > If you ask people to randomly place dots on a piece of paper what you
>>> > get are a lot of rather evenly spaced dots. This is a good example of
>>> > how people misunderstand randomness. Another way we see it is worry over
>>> > cancer clusters, meaning epidemiologists are forever having to explain
>>> > to people that randomness tends to be rather clumpy and as such the vast
>>> > majority of 'clusters' are to be expected.

The people are quite correct to be sceptical or worried if the power of the
study is not clearly explained.

Absense of evidence (or insufficient data for significance) is not evidence of absense (or lack of causal link).

Toby
.

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