Publications by authors named "Paul Murtaugh"

Statistical hypothesis testing has been widely criticized by ecologists in recent years. I review some of the more persistent criticisms of P values and argue that most stem from misunderstandings or incorrect interpretations, rather than from intrinsic shortcomings of the P value. I show that P values are intimately linked to confidence intervals and to differences in Akaike's information criterion (deltaAIC), two metrics that have been advocated as replacements for the P value.

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We introduce two simple methods for the statistical comparison of the temporal pattern of life-cycle events between two populations. The methods are based on a translation of stage-frequency data into individual 'times in stage'. For example, if the stage-k individuals in a set of samples consist of three individuals counted at time t(1) and two counted at time t(2), the observed times in stage k would be (t(1), t(1), t(1), t(2), t(2)).

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I evaluated the predictive ability of statistical models obtained by applying seven methods of variable selection to 12 ecological and environmental data sets. Cross-validation, involving repeated splits of each data set into training and validation subsets, was used to obtain honest estimates of predictive ability that could be fairly compared among methods. There was surprisingly little difference in predictive ability among five methods based on multiple linear regression.

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Context: Sex hormone fluctuations have been implicated as a contributing factor to the high rates of noncontact injury to the anterior cruciate ligament in females.

Objective: To determine the strength of the relationships among variables of sex hormone concentrations, motoneuron excitability, and anterior tibial displacement (ATD) in women and men and to determine if these relationships differ between the sexes.

Design: Cohort study.

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I argue that ecological data analyses are often needlessly complicated, and I present two examples of published analyses for which simpler alternatives are available. Unnecessary complexity is often introduced when analysts focus on subunits of the key experimental or observational units in a study, or use a very general framework to present an analysis that is a simple special case. Simpler analyses are easier to explain and understand; they clarify what the key units in a study are; they reduce the chances for computational mistakes; and they are more likely to lead to the same conclusions when applied by different analysts to the same data.

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We use data from a survey of several hundred lakes in the northeastern United States by the U.S. Environmental Protection Agency to illustrate an approach to identifying promising indicators of lake condition.

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