----- Original Message -----
From: Michael Granaas <[EMAIL PROTECTED]>
> Our current verbal lables leave much to be
desired.
>
> Depending on who you ask the "null hypothesis" is
>
> a) a hypothesis of no effect (nil hypothesis)
> b) an a priori false hypothesis to be rejected (straw dog hypothesis)
> c) an a priori plausible hypothesis to be tested and falsified or
> corroborated (wish I had a term for this usage/real null?)
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>
> Depending on who you ask the "null hypothesis" is
>
> a) a hypothesis of no effect (nil hypothesis)
> b) an a priori false hypothesis to be rejected (straw dog hypothesis)
> c) an a priori plausible hypothesis to be tested and falsified or
> corroborated (wish I had a term for this usage/real null?)
----------------------------------------------------------------------------------
The concept of a hypothesis is important. It can be used to
teach an important statistical concept.
Let us supose there are many plausible hypotheses. These
include the "nil hypothesis" any priori hypotheses any idea at all that may be
considered. Refer to these in terms of set of all plausible hypothesis
(including that of no effect) that are to be tested.
The process is to pick each hypothesis and test it. The
outcome of the test is not only a probability, but a reality check (the
investigators belief system). THE OUTCOME CAN ONLY BE BINARY, REJECTION OR
NON-REJECTION. Non-rejection is not acceptance. It just means that under
non-rejection, the hypothesis is in the set of all hypotheses that were
not rejected. The process does not pick out the true hypothesis, it never can do
that. It can only reject those hypothesis that have little chance of fitting the
data. You can ignor them then. You have to use other techniques to pick the
acceptable hypothesis out of all those in the 'not rejected" set. Any "verbal or
mathematical summary" is acceptable (that is in the set of non-rejected
hypothesis) (Pearson 1892, p22).
As R.A. Fisher said (re. a level of 0.05 level of significance
in testing a hypotheses) "does not mean that he allows himself to be deceived
once in twenty experiments. The test of significance only tells him what to
ignor, namely all experiments in which significant test results are nto
obtained" (Fisher 1929b, p 191). Fisher also said "a test of significance
contains no criteria for 'accepting' a hypothesis' (Fisher 1937, p
45).
DAHeiser
