In article <004101bfa35b$54beb900$[EMAIL PROTECTED]>,
David A. Heiser <[EMAIL PROTECTED]> wrote:
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>----- 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?)
>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 set of all plausible hypotheses is generally uncountable,
even in the discrete case.
>The process is to pick each hypothesis and test it.
This cannot be done; there are too many.
The outcome of the
>test is not only a probability, but a reality check (the investigators
>belief system).
The data consists of what has been observed. The likelihood
principle then mandates that the probabilities of unobserved
events becomes irrelevant. This means that the typical test
procedures (NOT the test STATISTICS) would have to be wrong.
--
This address is for information only. I do not claim that these views
are those of the Statistics Department or of Purdue University.
Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399
[EMAIL PROTECTED] Phone: (765)494-6054 FAX: (765)494-0558
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