Statistical Power Analysis in Wildlife Research

Steidl, Robert J.; Hayes, John P.; Schauber, Eric M.

doi:10.2307/3802582

Cited by 343 publications

(222 citation statements)

References 35 publications

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“…analysis provides useful information, but retrospective power analysis (that uses only the data from a single experiment) provides no additional information that is not already implicit in the p value (e.g., Gerard, Smith, & Weerakkody, 1998;Hoenig & Heisey, 2001;Nakagawa & Foster, 2004;O'Keefe, 2007;Steidl, Hayes, & Schauber, 1997;Sun, Pan, & Wang, 2011;Thomas, 1997). Retrospective power analysis can, however, at least make explicit the probability of achieving various goals in the given experiment, even if that information is not useful for additional inference from the given data.…”

Section: Power Analysis For Bayesian Estimationmentioning

confidence: 99%

Bayesian Estimation Supersedes the t Test

Kruschke¹

2012

PsycEXTRA Dataset

316

412

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Bayesian estimation for 2 groups provides complete distributions of credible values for the effect size, group means and their difference, standard deviations and their difference, and the normality of the data. The method handles outliers. The decision rule can accept the null value (unlike traditional t tests) when certainty in the estimate is high (unlike Bayesian model comparison using Bayes factors). The method also yields precise estimates of statistical power for various research goals. The software and programs are free and run on Macintosh, Windows, and Linux platforms.Keywords: Bayesian statistics, effect size, robust estimation, Bayes factor, confidence interval One of the most frequently encountered scientific procedures is a comparison of two groups (e.g., du Prel, Röhrig, Hommel, & Blettner, 2010;Fritz, Morris, & Richler, 2012;Wetzels et al., 2011). Given data from two groups, researchers ask various comparative questions: How much is one group different from another? Can we be reasonably sure that the difference is non-zero? How certain are we about the magnitude of difference? These questions are difficult to answer because data are contaminated by random variability despite researchers' efforts to minimize extraneous influences on the data. Because of "noise" in the data, researchers rely on statistical methods of probabilistic inference to interpret the data. When data are interpreted in terms of meaningful parameters in a mathematical description, such as the difference of mean parameters in two groups, it is Bayesian analysis that provides complete information about the credible parameter values. Bayesian analysis is also more intuitive than traditional methods of null hypothesis significance testing (e.g., Dienes, 2011).This article introduces an intuitive Bayesian approach to the analysis of data from two groups. The method yields complete distributional information about the means and standard deviations of the groups. In particular, the analysis reveals the relative credibility of every possible difference of means, every possible difference of standard deviations, and all possible effect sizes. From this explicit distribution of credible parameter values, inferences about null values can be made without ever referring to p values as in null hypothesis significance testing (NHST). Unlike NHST, the Bayesian method can accept the null value, not only reject it, when certainty in the estimate is high. The new method handles outliers by describing the data as heavy tailed distributions instead of normal distributions, to the extent implied by the data. The new method also implements power analysis in both retrospective and prospective forms.The analysis is implemented in the widely used and free programming languages R and JAGS and can be run on Macintosh, Linux, and Windows operating systems. Complete installation instructions are provided, along with working examples. The programs can also be flexibly extended to other types of data and analyses. Thus, the software can be used by virtually ...

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Section: Power Analysis For Bayesian Estimationmentioning

confidence: 99%

Bayesian Estimation Supersedes the t Test

Kruschke¹

2012

PsycEXTRA Dataset

316

412

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“…(Steidl et al 1997). Les résultats ne montrent donc pas d'augmentation observable et mesurable de la croissance des tiges des peuplements éclaircis suite à l'intervention.…”

Section: Sapin Baumierunclassified

Effets d'éclaircies précommerciales et de la dernière épidémie de la tordeuse des bourgeons de l'épinette (Choristoneura fumiferana (Clem.)) sur la croissance d'épinettes et de sapins dans la région du Saguenay-Lac-Saint-Jean /

Tremblay¹

2006

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“…14, 2003 ment conventional VHF telemetry projects, or pro¬ ceed with the maximum number of collars that can be maintained within a specified budget. Such con¬ straints lead to reduced statistical power of subse¬ quent data analyses (Steidl et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

“…To date, however, there has been no effort to estimate collar sample size requirements for this type of program, central to determining effectiveness of mitigation efforts. Additionally, sta¬ tistical analyses could suffer from lack of power (Steidl et al, 1997). It is noteworthy that earlier studies have identified potential negative impacts of the jet activity on caribou (Harrington & Veitch, 1991;Harrington & Veitch, 1992).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of satellite collar sample size requirements for mitigation of low-level military jet disturbance of the George River caribou herd

Otto¹,

Simon²,

Couturier³

et al. 2003

Ran

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Wildlife radio-telemetry and tracking projects often determine a priori required sample sizes by statistical means or default to the maximum number that can be maintained within a limited budget. After initiation of such projects, little attention is focussed on effective sample size requirements, resulting in lack of statistical power. The Department of National Defence operates a base in Labrador, Canada for low level jet fighter training activities, and maintain a sample of satellite collars on the George River caribou (Rangifer tarandus caribou) herd of the region for spatial avoidance mitiga¬ tion purposes. We analysed existing location data, in conjunction with knowledge of life history, to develop estimates of satellite collar sample sizes required to ensure adequate mitigation of GRCH. We chose three levels of probability in each of six annual caribou seasons. Estimated number of collars required ranged from 15 to 52, 23 to 68, and 36 to 184 for 50%, 75%, and 90% probability levels, respectively, depending on season. Estimates can be used to make more informed decisions about mitigation of GRCH, and, generally, our approach provides a means to adaptively assess radio collar sam¬ ple sizes for ongoing studies.

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Statistical Power Analysis in Wildlife Research

Cited by 343 publications

References 35 publications

Bayesian Estimation Supersedes the t Test

Bayesian Estimation Supersedes the t Test

Effets d'éclaircies précommerciales et de la dernière épidémie de la tordeuse des bourgeons de l'épinette (Choristoneura fumiferana (Clem.)) sur la croissance d'épinettes et de sapins dans la région du Saguenay-Lac-Saint-Jean /

Evaluation of satellite collar sample size requirements for mitigation of low-level military jet disturbance of the George River caribou herd

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