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This article describes the origins of the conventional use of null hypothesis significance testing and why this convention has led to difficulties in implementing research results in applied settings. This article continues to explain the the value of expressing research results with confidence limits and effect sizes for sporting application. As researchers investigating human performance, one of our greatest measures of success is for our research outcome to be implemented by sports coaches for their athletes. While coaches are becoming increasingly receptive to the results of sports scientists, coaches are often frustrated by the incon- clusive and numerically cryptic results we report. Conven- tional null hypothesis significance testing dictates that unless the probability of rejecting the null in error (p-value) is less than 5%, we must accept the null hypothesis that the differ- ence between our groups is zero. But to return to a sports coach after six weeks of a training intervention to report "nothing happened" is frustrating and probably not entirely accurate. It may be possible that the intervention did have an effect, but due to sources of error in human performance testing, the results lacked sufficient consistency to pass the conventional 5% rule. However, is the p-value returned by our results greater than 5% because nothing happened, or is the problem in our use of the arbitrary 5% line in the sand to justify the success or failure of our intervention? After all, "… surely, God loves the .06 nearly as much as the .05." (1, p. 1277). Origins of the p-values in Null Hypothesis Significance Testing Initially describing type I and type II error rates was the work of Neyman and Pearson (2). Neyman and Pearson con- sidered that there was sufficient evidence to reject a null hy- pothesis if the probability of its rejection in error was less than 5%. The work by Fisher (3) initially described some standard levels (e.g. 1%, 5%, 10%, etc.) of area under the  2 , t- and f-distributions, thereby making 5% of these distribu- tions widely accessible to researchers. While Fisher only intended percentages of these distributions to add support to inferences drawn from data, Neyman and Pearson argued that in order for research to be used to make decisions, 5% of these distributions was an acceptable  (cut-off point) (4). Since this time, accepting or rejecting a null hypothesis based on a 5% probability of error has become the norm. The sport science interpretation of Neyman and Pearson's work would be that sports coaches (i.e. research end-users) can only make informed decisions when told if an intervention works or does not work, whereas Fisher would argue that sports coaches should be the ones to decide what probability of error is unacceptably high for their athletes (4).

Applications of Confidence Limits and Effect Sizes in Sport Research