Assessing group-based changes in high-performance sport. Part 2: effect sizes and embracing uncertainty through confidence intervals

Today’s strength and conditioning coach must extend their skill set to include data analysis, understating the validity and utility of p values, effect sizes, confidence intervals, and terms such as the smallest worthwhile change, and minimal difference. The aim of part two of this two-part review is to now build on our discussion of null hypothesis significance testing (covered in part one), and introduce effect sizes, measures of variability, and confidence intervals, culminating in recommendations as to which may be the most viable options within the context of performance-based sport, and thus potential methods to report group-based changes. This paper has a series of worked examples to aid the reader

Assessing group-based changes in high-performance sport. Part 1: null hypothesis significance testing and the utility of p values

The role of a strength and conditioning coach (SCC) has evolved over the last 10 years to accommodate the large influx of data now available. As such, today’s SCC must extend their skill set to include data analysis, understanding the validity and utility of p values, effect sizes, confidence intervals, and terms such as the smallest worthwhile change, and minimal difference. The aim of part one of this two-part review is to define and discuss the utility of null hypothesis significance testing (NHST), p values, and error rates. In part two, we introduce effect sizes, measures of variability, and confidence intervals, culminating in recommendations as to which may be the most viable options within the context of performance-based sport, and thus potential methods to report group-based changes