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Vinson H. Sutlive and Dale A. Ulrich

The unqualified use of statistical significance tests for interpreting the results of empirical research has been called into question by researchers in a number of behavioral disciplines. This paper reviews what statistical significance tells us and what it does not, with particular attention paid to criticisms of using the results of these tests as the sole basis for evaluating the overall significance of research findings. In addition, implications for adapted physical activity research are discussed. Based on the recent literature of other disciplines, several recommendations for evaluating and reporting research findings are made. They include calculating and reporting effect sizes, selecting an alpha level larger than the conventional .05 level, placing greater emphasis on replication of results, evaluating results in a sample size context, and employing simple research designs. Adapted physical activity researchers are encouraged to use specific modifiers when describing findings as significant.

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Daniel Weimar, Brian P. Soebbing, and Pamela Wicker

samples are more likely to yield statistically significant effects. The second purpose is to apply dominance analysis, which provides further information beyond statistical significance by assessing the relative impact of determinants. Specifically, panel regression analysis should be followed by

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Bruce Thompson

The milestones in the movement of the field away from emphasizing p values, and toward emphasizing effect size reporting, are reviewed. The primer also briefly introduces the effect size types and recommends a few effect size usage and interpretation guidelines that journals and authors should follow.

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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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Nicolas Farina, Laura J. Hughes, Serena Thomas, Ruth G. Lowry, and Sube Banerjee

those with mild to moderate dementia, weak positive associations were observed between physical activity and HRQL across the majority of outcomes. Only the association between time spent physically active per week (CHAMPs) and EQ-5D index reached statistical significance (see Table  2 ). Table 2

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Rebecca M. Dagger, Ian G. Davies, Kelly A. Mackintosh, Genevieve L. Stone, Keith P. George, Stuart J. Fairclough, and Lynne M. Boddy

composition (DEXA) measures showed favorable improvements in the intervention group in comparison to the control group in a range of measures (Tables  2 and 3 ); however, none of these changes with the exception of mean trunk fat and trunk fat % reached statistical significance ( P  > .05). Whole-body fat

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Sari Aaltonen, Teemu Palviainen, Richard J. Rose, Urho M. Kujala, Jaakko Kaprio, and Karri Silventoinen

mechanism underlying the association. The next highest genetic correlations were found between LTPA and reading aloud as well as between LTPA and mathematics (both r A  = .11), but these findings did not reach statistical significance. Even though the importance of familial factors in explaining the

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Caleb D. Johnson and Irene S. Davis

adjusting for the effects of sex, based on the results of analysis of covariance. Figure 1 —Mean impulse ratios by arch flexibility grouping and foot strike pattern. Error bars represent 95% confidence intervals. FFS indicates forefoot strikers; RFS, rearfoot strikers. *Statistical significance at P  < .05

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Samuel G. Wittekind, Nicholas M. Edwards, Philip R. Khoury, Connie E. McCoy, Lawrence M. Dolan, Thomas R. Kimball, and Elaine M. Urbina

total cpm + 0.052 × mean arterial pressure + 0.069 × age − 0.9 (if lean control) − 0.64 (if obese control) − 0.36 (if white); R 2  = .47] but lost statistical significance when adjusted for metabolic variables such as lipid levels and glycemic control [regression equation: PWV = −0.15 + 0.04 × mean

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Agustín Manresa-Rocamora, José Manuel Sarabia, Julio Sánchez-Meca, José Oliveira, Francisco Jose Vera-Garcia, and Manuel Moya-Ramón

significance ( p  = .590). However, tests for subgroup differences among the three average MDs did show statistical significance ( p  < .001) with large heterogeneity explained ( I 2  = 98.9%). Therefore, we carried out pairwise comparisons. We performed a Bonferroni correction to control the increase in Type