
The Null Ritual (2004) [pdf] - zby
https://library.mpib-berlin.mpg.de/ft/gg/GG_Null_2004.pdf
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AstralStorm
Medical science sometimes escapes the wrong approach by actually providing
odds ratios (conditional probability estimate between events or phones) or
effect sizes including confidence bounds on these. That is already useful
information about the hypothesis being tested, as opposed to significance
which is information about validity of data.

However, an explicit estimate of predictive power (sensitivity) would be very
useful too - confidence intervals do not provide information on the model used
to derive them which may or may not fit. Instead one has to use proxies like
experimental design descriptions and N.

~~~
nonbel
> "That is already useful information about the hypothesis being tested"

The important part is to choose the correct hypothesis to test. However, there
is no requirement to use the effect size to test any hypothesis, although
eventually someone should come up with a model that explains why it has the
value it does.

> "as opposed to significance which is information about validity of data."

Huh?

~~~
AstralStorm
I meant the p-value comparison to cutoff there. It is the test on probability
of data being observed given that null hypothesis is true and error model
matches your assumption, typically assumed normal or studentized.

Such as by chance or because the alternate hypothesis is bunkum or because
your experiment lacks power. (It does not distinguish between these, and
middle item is only minimally handled by most waste of obtaining it, esp. not
by Fischer's or ANOVA which are facts about data only, using error in
measurement model.)

Thus p-value is a statement about validity of data (type 1 error, false
positive) and maybe about the null hypothesis as a secondary notion.

(E.g. about the control group of you use that as reference group for null.)

