%0 Journal Article %1 stephens_inference_2007 %A Stephens, Philip A. %A Buskirk, Steven W. %A del Rio, Carlos Martínez %D 2007 %J Trends in Ecology & Evolution %K AIC, approach, cited, highly inference information review, statistical theoretic %N 4 %P 192--197 %R 10.1016/j.tree.2006.12.003 %T Inference in ecology and evolution %U http://linkinghub.elsevier.com/retrieve/pii/S0169534706004009 %V 22 %X Most ecologists and evolutionary biologists continue to rely heavily on null hypothesis significance testing, rather than on recently advocated alternatives, for inference. Here, we briefly review null hypothesis significance testing and its major alternatives. We identify major objectives of statistical analysis and suggest which analytical approaches are appropriate for each. Any well designed study can improve our understanding of biological systems, regardless of the inferential approach used. Nevertheless, an awareness of available techniques and their pitfalls could guide better approaches to data collection and broaden the range of questions that can be addressed. Although we should reduce our reliance on significance testing, it retains an important role in statistical education and is likely to remain fundamental to the falsification of scientific hypotheses.

@article{stephens_inference_2007, abstract = {Most ecologists and evolutionary biologists continue to rely heavily on null hypothesis significance testing, rather than on recently advocated alternatives, for inference. Here, we briefly review null hypothesis significance testing and its major alternatives. We identify major objectives of statistical analysis and suggest which analytical approaches are appropriate for each. Any well designed study can improve our understanding of biological systems, regardless of the inferential approach used. Nevertheless, an awareness of available techniques and their pitfalls could guide better approaches to data collection and broaden the range of questions that can be addressed. Although we should reduce our reliance on significance testing, it retains an important role in statistical education and is likely to remain fundamental to the falsification of scientific hypotheses.}, added-at = {2017-01-09T13:57:26.000+0100}, author = {Stephens, Philip A. and Buskirk, Steven W. and del Rio, Carlos Martínez}, biburl = {https://www.bibsonomy.org/bibtex/22ba3e0836ed1745087a9b1b7c70285e8/yourwelcome}, doi = {10.1016/j.tree.2006.12.003}, interhash = {add3250e4b878def4ac879736213b027}, intrahash = {2ba3e0836ed1745087a9b1b7c70285e8}, issn = {01695347}, journal = {Trends in Ecology \& Evolution}, keywords = {AIC, approach, cited, highly inference information review, statistical theoretic}, month = apr, number = 4, pages = {192--197}, timestamp = {2017-01-09T14:01:11.000+0100}, title = {Inference in ecology and evolution}, url = {http://linkinghub.elsevier.com/retrieve/pii/S0169534706004009}, urldate = {2013-06-24}, volume = 22, year = 2007 }