Null Hypothesis Testing: Problems, Prevalence, and an Alternative

Anderson, David R.; Burnham, Kenneth P.; Thompson, William L.

doi:10.2307/3803199

Cited by 1,618 publications

(1,308 citation statements)

References 39 publications

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“…ANOVA or linear regression) is not always intuitive. Hence, we offer some guidance about how to get from multiple working hypotheses to a set of candidate models [2,6].…”

Section: Box 2 From Multiple Working Hypotheses To a Set Of Candidatmentioning

confidence: 99%

“…Not surprisingly, such conditions are typical of many research problems in ecology and evolution, particularly when experimental manipulation is not possible. Unfortunately, null hypothesis testing remains the dominant mode of inference in ecology and evolution [2], even for studies that are best suited to the model selection approach. We illustrate this with two examples.…”

Section: Use Of Model Averagingmentioning

confidence: 99%

“…The dominant paradigm is to generate a null hypothesis (typically one with little biological meaning [2]) and ask whether the hypothesis can be rejected in light of observed data. Rejection occurs when a test statistic generated from observed data falls beyond an arbitrary probability threshold (usually P , 0.05), which is interpreted as tacit support for a biologically more meaningful alternative hypothesis.…”

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Model selection in ecology and evolution

Johnson¹,

Omland²

2004

Trends in Ecology & Evolution

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Recently, researchers in several areas of ecology and evolution have begun to change the way in which they analyze data and make biological inferences. Rather than the traditional null hypothesis testing approach, they have adopted an approach called model selection, in which several competing hypotheses are simultaneously confronted with data. Model selection can be used to identify a single best model, thus lending support to one particular hypothesis, or it can be used to make inferences based on weighted support from a complete set of competing models. Model selection is widely accepted and well developed in certain fields, most notably in molecular systematics and markrecapture analysis. However, it is now gaining support in several other areas, from molecular evolution to landscape ecology. Here, we outline the steps of model selection and highlight several ways that it is now being implemented. By adopting this approach, researchers in ecology and evolution will find a valuable alternative to traditional null hypothesis testing, especially when more than one hypothesis is plausible.Science is a process for learning about nature in which competing ideas about how the world works are evaluated against observations [1]. These ideas are usually expressed first as verbal hypotheses, and then as mathematical equations, or models. Models depict biological processes in simplified and general ways that provide insight into factors that are responsible for observed patterns. Hence, the degree to which observed data support a model also reflects the relative support for the associated hypothesis.Two basic approaches have been used to draw biological inferences. The dominant paradigm is to generate a null hypothesis (typically one with little biological meaning [2]) and ask whether the hypothesis can be rejected in light of observed data. Rejection occurs when a test statistic generated from observed data falls beyond an arbitrary probability threshold (usually P , 0.05), which is interpreted as tacit support for a biologically more meaningful alternative hypothesis. Hence, the actual hypothesis of interest (the alternative hypothesis) is accepted only in the sense that the null hypothesis is rejected. By contrast, model selection offers a way to draw inferences from a set of multiple competing hypotheses. Model selection is grounded in likelihood theory, a robust framework that supports most modern statistical approaches. Moreover, this approach is rapidly gaining support across several fields in ecology and evolution as a preferred alternative to null hypothesis testing [1,3,4]. Advocates of model selection argue that it has three primary advantages. First, practitioners are not restricted to evaluating a single model where significance is measured against some arbitrary probability threshold. Instead, competing models are compared to one another by evaluating the relative support in the observed data for each model. Second, models can be ranked and weighted, thereby providing a quantitative measure of relative sup...

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“…ANOVA or linear regression) is not always intuitive. Hence, we offer some guidance about how to get from multiple working hypotheses to a set of candidate models [2,6].…”

Section: Box 2 From Multiple Working Hypotheses To a Set Of Candidatmentioning

confidence: 99%

Section: Use Of Model Averagingmentioning

confidence: 99%

mentioning

confidence: 99%

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Model selection in ecology and evolution

Johnson¹,

Omland²

2004

Trends in Ecology & Evolution

3,117

2,622

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show abstract

“…The best model was selected, by using the Akaike Information Criterion (AIC), as the model with the lowest AIC (Franklin et al, 2001). Each model was weighed against the others using the Akaike weights, which gives an estimation of the likelihood of the model's fit according to the employed data (Anderson et al, 2000;Franklin et al, 2001;Johnson & Omland, 2004).…”

Section: Fecundity Estimation and Gonadosomatic Index (Gsi%)mentioning

confidence: 99%

Reproduction of Brevoortia aurea (Spix & Agassiz, 1829) (Actinopterygii: Clupeidae) in the Mar Chiquita Coastal Lagoon, Buenos Aires, Argentina

2016

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Reproductive biology of the Brazilian menhaden, Brevoortia aurea (Clupeiformes), was studied in Mar Chiquita Coastal Lagoon. Its abundance was analysed in relation to environmental variables, and the main biological-reproductive parameters were estimated: size at first maturity, batch and relative fecundity, frequency distribution of oocyte diameters and gonadosomatic index (GSI). Moreover, its reproductive cycle was analysed histologically. Samples were collected from May 2012 to April 2013. Once the spawning period was detected, extra samples were added (October and November 2013). The specimens were caught with gillnets, at a distance of 2,300 meters from the mouth of the lagoon. The highest abundances were observed in the months of October and November. Both macroscopic and microscopic analysis allowed to confirm that in these months the saraca spawn in the mixo-eurihaline area of the lagoon. Females were recorded in the five phases of ovarian development. Histological and frequency distribution of oocyte diameters characterized this species as a batch spawner with undetermined fecundity. The fecundity varied between 19,900 and 178,508 oocytes/female. Relative fecundity ranged between 41 and 381 oocytes/g female ovary free. The size at first maturity was estimated on 277 and 265 mm total length for females and males, respectively.

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“…Although useful, here we decided to focus on BHT as this is a Bayesian alternative to NHST, which is commonly used in our field. For thorough discussions of the advantages and disadvantages of different alternatives to NHST, we refer to Anderson, Burnham, and Thompson (2000); Dienes (2008); Gardner and Altman (1986) and Wagenmakers et al (2016). …”

Section: Discussionmentioning

confidence: 99%

Bayesian hypothesis testing for human threat conditioning research: an introduction and the condir R package

Krypotos

Klugkist

Engelhard

2017

European Journal of Psychotraumatology

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Threat conditioning procedures have allowed the experimental investigation of the pathogenesis of Post-Traumatic Stress Disorder. The ﬁndings of these procedures have also provided stable foundations for the development of relevant intervention programs (e.g. exposure therapy). Statistical inference of threat conditioning procedures is commonly based on p-values and Null Hypothesis Signiﬁcance Testing (NHST). Nowadays, however, there is a growing concern about this statistical approach, as many scientists point to the various limitations of p-values and NHST. As an alternative, the use of Bayes factors and Bayesian hypothesis testing has been suggested. In this article, we apply this statistical approach to threat conditioning data. In order to enable the easy computation of Bayes factors for threat conditioning data we present a new R package named condir, which can be used either via the R console or via a Shiny application. This article provides both a non-technical introduction to Bayesian analysis for researchers using the threat conditioning paradigm, and the necessary tools for computing Bayes factors easily.

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Null Hypothesis Testing: Problems, Prevalence, and an Alternative

Cited by 1,618 publications

References 39 publications

Model selection in ecology and evolution

Model selection in ecology and evolution

Reproduction of Brevoortia aurea (Spix & Agassiz, 1829) (Actinopterygii: Clupeidae) in the Mar Chiquita Coastal Lagoon, Buenos Aires, Argentina

Bayesian hypothesis testing for human threat conditioning research: an introduction and the condir R package

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