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Persistent Controversy in Statistical Approaches in Wildlife Sciences: A Perspective of Students
Author(s) -
BUTCHER JERROD A.,
GROCE JULIE E.,
LITUMA CHRISTOPHER M.,
COCIMANO M. CONSTANZA,
SÁNCHEZJOHNSON YARA,
CAMPOMIZZI ANDREW J.,
POPE THERESA L.,
REYNA KELLY S.,
KNIPPS ANNA C. S.
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 - 10.2193/2007-201
Subject(s) - wildlife , frequentist inference , null hypothesis , statistical hypothesis testing , perspective (graphical) , publication , statistical inference , data science , psychology , ecology , bayesian probability , computer science , bayesian inference , statistics , mathematics , artificial intelligence , political science , biology , law
ABSTRACT The controversy over the use of null hypothesis statistical testing (NHST) has persisted for decades, yet NHST remains the most widely used statistical approach in wildlife sciences and ecology. A disconnect exists between those opposing NHST and many wildlife scientists and ecologists who conduct and publish research. This disconnect causes confusion and frustration on the part of students. We, as students, offer our perspective on how this issue may be addressed. Our objective is to encourage academic institutions and advisors of undergraduate and graduate students to introduce students to various statistical approaches so we can make well‐informed decisions on the appropriate use of statistical tools in wildlife and ecological research projects. We propose an academic course that introduces students to various statistical approaches (e.g., Bayesian, frequentist, Fisherian, information theory) to build a foundation for critical thinking in applying statistics. We encourage academic advisors to become familiar with the statistical approaches available to wildlife scientists and ecologists and thus decrease bias towards one approach. Null hypothesis statistical testing is likely to persist as the most common statistical analysis tool in wildlife science until academic institutions and student advisors change their approach and emphasize a wider range of statistical methods.