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Statistical Versus Biological Hypothesis Testing: Response to Steidl
Author(s) -
SLEEP D.J.H.,
DREVER M.C.,
NUDDS T.D.
Publication year - 2007
Publication title -
the journal of wildlife management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.94
H-Index - 111
eISSN - 1937-2817
pISSN - 0022-541X
DOI - 10.2193/2007-140
Subject(s) - frequentist inference , akaike information criterion , statistical hypothesis testing , null hypothesis , statistical inference , alternative hypothesis , econometrics , inference , statistics , psychology , computer science , mathematics , artificial intelligence , bayesian inference , bayesian probability
In spite of the wide use and acceptance of information theoretic approaches in the wildlife sciences, debate continues on the correct use and interpretation of Akaike's Information Criterion as compared to frequentist methods. Misunderstandings as to the fundamental nature of such comparisons continue. Here we agree with Steidl's argument about situation‐specific use of each approach. However, Steidl did not make clear the distinction between statistical and biological hypotheses. Certainly model selection is not statistical, or null, hypothesis testing; importantly, it represents a more effective means to test among competing biological, or research, hypotheses. Employed correctly, it leads to superior strength of inference and reduces the risk that favorite hypotheses are uncritically accepted.