AGAINST

AGAINST
Brett Magil and Jack Tomsky
(That are unlearned at a scandalous point.)
IN FACT it can be read in Wikipedia
*** [accepting H0]: There is not enough evidence to reject the hypothesis. This is not the same as evidence in favor of the hypothesis. That we cannot obtain using these arguments, since lack of evidence against a hypothesis is not evidence for it. On this basis, statistical research progresses by eliminating error, not by finding the truth.***
My comment
OF COURSE every first course in Statistic student is aware of this pecularity:: teachers THAT KNOW WHAT ARE SAYING does not fail to point out this feature.
Adding to the same I referred yet on the WEB
*** If we conclude 'do not reject H0', this does not necessarily mean that the null hypothesis is true, it only suggests that there is not sufficient evidence against H0 in favour of H1; rejecting the null hypothesis then, suggests that the alternative hypothesis may be true.***
ADDING
A SIMPLE SAMPLE SIZE EVALUATION that all the Readers should make: (a problem I solved as a University student, here, in Portugal).
*** FROM a normal population N(0, 1) (that only we know that is normal and sigma=1, mean unknown) we intend draw a random sample. What should be the minimum size in order not to reject ___H0 : the mean is at most 0.000´001 at the 0.001 significance level (one tailed test, Z = 3.090), against___Ha : mean greater than 0.000´001. ***
NOTE THIS IS VERY DIFFERENT, (MUST LESS DEMANDING) THAN IF
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I INTENDED TO KNOW THAT THE MEAN IS ZERO , WHICH CANNOT BE REACHED for finte sample sizes. THEREFORE IT IS IMPOSSIBLE IN PRACTICE.
_____licas (Luis A. Afonso)
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