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Jos J. de Koning and Dionne A. Noordhof

know that it is not that simple. Is the winner really the best athlete? Did the training intervention give a performance benefit, and which performance-determining variable was improved? There is a lot of uncertainty in our day-to-day practice, while the world around us is asking for unambiguous

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Paul R. Surburg

The effects of uncertainty of occurrence and uncertainty of time—use of catch-trials and preparatory intervals—in simple reaction time (RT) trials were investigated with nonhandicapped and mentally retarded subjects. The results showed that: (a) Catch-trials impaired the performance of this task, (b) catch-trials did not differentiate among groups of subjects, and (c) preparatory intervals differently affected RT latencies of nonhandicapped and mentally retarded subjects. Interpretation of findings suggests that the use of catch-trials induced preparation decrements and that preparation decrements may explain in part the poorer RT performance of retarded subjects.

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Daniel B. Robinson, Lynn Randall and Joe Barrett

early and often throughout teacher education training ( Newton & Bassett, 2013 ). After noting the uncertainty and confusion that exists in the literature around various terms associated with PE (e.g., PE, health and PE, physical literacy, and health literacy), Lynch and Soukup ( 2016 ) investigated

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Alfred Nimmerichter, Bernhard Prinz, Matthias Gumpenberger, Sebastian Heider and Klaus Wirth

-subject SD from the TT20 multiplied by a constant factor of 0.2. 29 Differences between the predicted and the actual TT20 are presented with 90% confidence intervals as a measure of uncertainty in Figure  1 and interpreted in qualitative terms as follows 29 : <1%, “most unlikely”; 1% to 5%, “very unlikely

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Kacey C. Neely, John G.H. Dunn, Tara-Leigh F. McHugh and Nicholas L. Holt

The overall purpose of this study was to examine coaches’ views on deselecting athletes from competitive female adolescent sport teams. Individual semistructured interviews were conducted with 22 head coaches of Canadian provincial level soccer, basketball, volleyball, and ice hockey teams. Interpretive description methodology (Thorne, 2008) was used. Results revealed deselection was a process that involved four phases: pre-tryout meeting, evaluation and decision-making, communication of deselection, and post deselection reflections. Within the evaluation and decision-making phase coaches made programmed and nonprogrammed decisions under conditions of certainty and uncertainty. When faced with uncertainty coaches relied on intuition.

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Niall Casserly, Ross Neville, Massimiliano Ditroilo and Adam Grainger

. This enabled us to estimate the change over time and differences between groups on each performance parameter with baseline values and body mass change held constant. Intraclass correlation coefficients were measured to assess the reliability between CMJ and 10-m acceleration efforts. Uncertainty was

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Alan M. Batterham and William G. Hopkins

A study of a sample provides only an estimate of the true (population) value of an outcome statistic. A report of the study therefore usually includes an inference about the true value. Traditionally, a researcher makes an inference by declaring the value of the statistic statistically significant or non significant on the basis of a P value derived from a null-hypothesis test. This approach is confusing and can be misleading, depending on the magnitude of the statistic, error of measurement, and sample size. The authors use a more intuitive and practical approach based directly on uncertainty in the true value of the statistic. First they express the uncertainty as confidence limits, which define the likely range of the true value. They then deal with the real-world relevance of this uncertainty by taking into account values of the statistic that are substantial in some positive and negative sense, such as beneficial or harmful. If the likely range overlaps substantially positive and negative values, they infer that the outcome is unclear; otherwise, they infer that the true value has the magnitude of the observed value: substantially positive, trivial, or substantially negative. They refine this crude inference by stating qualitatively the likelihood that the true value will have the observed magnitude (eg, very likely beneficial). Quantitative or qualitative probabilities that the true value has the other 2 magnitudes or more finely graded magnitudes (such as trivial, small, moderate, and large) can also be estimated to guide a decision about the utility of the outcome.

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Kirsti Van Dornick and Nancy L.I. Spencer

were unavoidable, yet questions about fairness were prominent. (Un)certainty Despite the concerns associated with representing functional diversity fairly, several paraswimmers indicated satisfaction with their sport class, feeling it was an accurate representation of their abilities. However

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Doune Macdonald and Ross Brooker

Recent literature suggests that secondary school physical education is in crisis due to uncertainties about focus, status, and accountability. After providing some background discussion to the crises, two curriculum approaches, one current and the other in trial, to secondary physical education in an Australian context are reviewed. Drawing upon empirical research, the various strengths and weaknesses of each approach are highlighted. The paper concludes with proposals that the movement-centered conceptualization of physical education in the trial approach offers a defensible physical education for secondary school students.

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Richard J. Barker and Matthew R. Schofield

In a recent commentary on statistical inference, Batterham and Hopkins1 advocated an approach to statistical inference centered on expressions of uncertainty in parameters. After criticizing an approach to statistical inference driven by null hypothesis testing, they proposed a method of “magnitude-based” inference and then claimed that this approach is essentially Bayesian but with no prior assumption about the true value of the parameter. In this commentary, after we address the issues raised by Batterham and Hopkins, we show that their method is “approximately” Bayesian and rather than assuming no prior information their approach has a very specific, but hidden, joint prior on parameters. To correctly adopt the type of inference advocated by Batterham and Hopkins, sport scientists need to use fully Bayesian methods of analysis.