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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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Heidi R. Thornton, André R. Nelson, Jace A. Delaney, Fabio R. Serpiello, and Grant M. Duthie

-manufacturer interunit reliability was calculated using a customized spreadsheet 17 to derive the CV (%) with associated uncertainty (±90% confidence limits [90% CL]), which were rated as: good (CV < 5%), moderate (CV = 5–10%), or poor (CV > 10%). 18 The signal in each variable (as described above) was reported as a

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Sharna A. Naidu, Maurizio Fanchini, Adam Cox, Joshua Smeaton, Will G. Hopkins, and Fabio R. Serpiello

  HR = 3 Zone   2 ; 60 − 69 %      peak   HR = 2 Zone   1 ; 50 − 59 %      peak   HR = 1 Statistical Analysis The (convergent) construct validity of sRPE was assessed via Pearson’s correlation coefficient and 90% confidence limits between sRPE and Edwards’ TL, and between sRPE and Banister’s TRIMP

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Daniel J. Peart, Michael Graham, Callum Blades, and Ian H. Walshe

resulted in a coefficient of variance of 3% over 3 visits, with a minimal worthwhile change of 7 lights between rounds (one more than the upper 95% confidence interval of the typical error). Data Analysis Differences (mean ± 90% confidence limits) between visits for each round were quantified using

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Jason C. Bartram, Dominic Thewlis, David T. Martin, and Kevin I. Norton

). Descriptive statistics were calculated for the end W ′ value using each model (mean, 95% confidence limits [CLs]). A repeated-measures analysis of variance was completed to assess differences in end W ′ values between the 3 models with post hoc Tukey tests. One-sample t tests were used to assess each

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Emily M. Partridge, Julie Cooke, Andrew J. McKune, and David B. Pyne

Shuttle run times and fatigue index scores are presented in Figure  2 . There was no difference in the mean change in shuttle run performance after PBC exposure compared with the control group (standardized difference; −0.4 [5.9%], P  = .881, ES = 0.07 ± 0.58; mean ± 90% confidence limits). The shuttle

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Matthew D. Wright, Francisco Songane, Stacey Emmonds, Paul Chesterton, Matthew Weston, and Shaun J. Mclaren

). Figure 2 —Within-player correlations for sRPE-B and sRPE-L with global RPE. Standardized ( r ) and raw (β) effects are presented with the uncertainty expressed as ±90% confidence limits. AU indicates arbitrary units; dRPE, differential ratings of perceived exertion; RPE, ratings of perceived exertion

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Avish P. Sharma, David J. Bentley, Gaizka Mejuto, and Naroa Etxebarria

to assess the relationships between VO 2 max, PPO, and sprint performance and expressed with 90% confidence limits to denote imprecision. Standardized scores for the correlations were interpreted according to a following scale of magnitudes: <.1, trivial; .1 to .3, small; .3 to .5, moderate; and .5

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Paul G. Montgomery and Brendan D. Maloney

PlayerLoad·min −1 values in other court-based competition such as netball have been reported at ∼154 and 2.60 a.u., respectively. 4 Recent reports in female team handball showed that PlayerLoad·min −1 was 11.37 ± 0.49 a.u. (mean ± 90% confidence limits [CL]) and 9.71 ± 0.3 a.u. for small-sided training

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Naroa Etxebarria, Megan L. Ross, Brad Clark, and Louise M. Burke

.2, trivial; 0.2 to 0.6, small; 0.6 to 1.2, moderate; 1.2 to 2.0, large; and >2.0, very large. Precision of estimation was determined using 90% confidence limits (CL). When the magnitude of the standardized effect crossed the threshold of a small positive and small negative (±0.2), the change or difference