Making meaningful inferences about magnitudes.

By A. Batterham, W. Hopkins. International Journal of Sports Physiology and Performance, 2006

2,287 citations recorded by the source corpus.

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 nonsignificant 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.

Subfields: Economics and Econometrics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/17839a981b02b703d77b44a90a4818b20dae7345

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Citations in the corpus

Monitoring training status with HR measures: do all roads lead to Rome? (2014, background): “…to practitioners is whether the training-related change in HR measures could be important, i.e., whether their magnitude is actually greater than the smallest practical or meaningful change/effect (the so-called smallest worthwhile change, SWC) (Batterham and Hopkins, 2006; Hopkins et al., 2009).”
Validity and reliability of GPS for measuring instantaneous velocity during acceleration, deceleration, and constant motion (2012, methodology): “2 × between-participant standard deviation; Batterham & Hopkins, 2006 ) to the coefficient of variation.”
Progressive statistics for studies in sports medicine and exercise science. (2009, background): “A more realistic and intuitive approach to inferences is based on where the confidence interval lies in relation to threshold values for substantial effects rather than the null value (4).”
Effects of manipulations of player numbers vs. field dimensions on inter-individual coordination during small-sided games in youth football (2015, neutral): “Probabilities were calculated to assess whether true effects obtained represented substantial changes (Batterham and Hopkins, 2005).”
Testing and Training Anticipation Skills in Softball Fielders (2007, methodology): “[24, 25] Firstly, differences between playing levels and playing positions were evaluated using a one-way 18 Anticipatory Skill in Softball Fielders Sports Science 2-1_final 14/3/07 9:22 am Page 18”