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The entire procedure of hypothesis-testing, and not merely that part of it that consists of arguing from sample to population, is called induction in Peirce's later philosophy.
Therefore, it provides us a novel procedure for hypothesis testing of big data which can be carried out in the compressed domain.
This also holds on an epistemological level – due to the asymmetries in the ways false positives and false negatives are treated in the default procedures of hypotheses testing in both epidemiology and toxicology: the standard methodological tradition gives the reduction of type I error with 95% significance levels more emphasis than avoiding false negatives by securing sufficient power [ 42, 43].
The procedure of this Hypothesis Test is summarized as follows.
It is also interesting to develop procedures for hypothesis testing to incorporate misclassification effects along the lines of Bross (1954).
Experimental setup, description of baseline systems, and evaluation procedures of individual hypotheses mentioned above are presented in Section 4, while Section 5 presents experimental results.
Finally, derived P-values (Hypogeometric) are adjusted for multiple testing using false discovery rate control procedure (the number of hypotheses tested is equal to the number data sets available).
The procedure of Fisher's null hypothesis test was already discussed briefly in the foregoing.
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