Alternatives to statistical hypothesis testing in ecology: a guide to self teaching.

By Thompson Hobbs, Ray Hilborn. Ecological Applications, 2006

325 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/b960f7f4cd6dd7ebcd8fca80dd3d47a53eb2ca37

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Statistical significance and biological relevance: A call for a more cautious interpretation of results in ecology (2008, background): “…write papers with the wrong conclusions if we make a wrong interpretation of our statistical analyses, making the whole costly process pretty much useless, not to say counter-productive, if decision-making is affected by our wrong conclusions (Ellison, 1996; Wade, 2000; Hobbs and Hilborn, 2006).”
Rising complexity and falling explanatory power in ecology (2014, methodology): “Although the use of alternative statistical approaches to NHST is infrequent in the history of the discipline, the application of Bayesian analyses, information theoretic approaches (eg Akaike’s information criterion), and other alternative methods to NHST are on the rise in ecological research (Hob”
Quantitative approaches in climate change ecology (2011, background): “Many ecologists are trained in classical approaches more suited to testing effects in controlled experimental designs than in long-term observational data (Hobbs & Hilborn, 2006).”
Aquatic insects in a sea of desert: population genetic structure is shaped by limited dispersal in a naturally fragmented landscape (2013, background): “…information-theoretic approach is an alternative to traditional null hypothesis testing that has gained widespread use in the ecological sciences (Hobbs and Hilborn 2006, Grueber et al. 2011) and has been applied recently in the field of landscape genetics (Pavlacky Jr et al. 2009, Wang 2009,…”
Model selection and model averaging in behavioural ecology: the utility of the IT-AIC framework (2010, methodology): “…IT-AIC that arise from our second example include the ability of this technique to aid the identification of appropriate error structures (see also Hobbs and Hilborn 2006) and the flexibility of the approach to deal with data that do not conform to some of the more restrictive assumptions of many…”
Explaining the global pattern of protected area coverage: relative importance of vertebrate biodiversity, human activities and agricultural suitability (2008, methodology): “The model with the minimum AIC or AICc (denoted AICmin) was considered the ‘best’ model supported by the data, and all others were evaluated based on their difference from this minimum (DAIC = AICi ) AICmin) (Burnham & Anderson, 2002; Hobbs & Hilborn, 2006).”
Paradox, presumption and pitfalls in conservation biology: The importance of habitat change for amphibians and reptiles. (2007, unknown): “…alternative techniques that deal more effectively with uncertainty and emphasise thoughtful hypothesis formulation and model building as part of the enquiry process (e.g. information-theoretic approaches, Bayesian techniques and meta-analyses; Burnham and Anderson, 2002; Hobbs and Hilborn, 2006).”
Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression (2009, methodology): “This methodological point of view is also related to recent changes in the philosophy of data analysis, with the gradual replacement of classical null-hypothesis significance testing of models with alternative statistical approaches where the goal is to rank competing models in terms of information ”
Information-theoretic approaches to statistical analysis in behavioural ecology: an introduction (2010, background): “Practitioners in ecology are in favour of the IT methodology for model selection problems, because it is suitable for testing several competing hypotheses in concert, and is able to deal with model selection uncertainty and to provide quantitative scientific results based on data support (Johnson an”