Related Experiment Video
Updated: Aug 27, 2025

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
Published on: January 21, 2017
Interpreting magnitude of change in strength and conditioning: Effect size selection, threshold values and Bayesian
Paul Alan Swinton1, Katherine Burgess1, Andy Hall1
1School of Health Sciences, Robert Gordon University, Aberdeen, UK.
Abstract:
The magnitude of change following strength and conditioning (S&C) training can be evaluated comparing effect sizes to thresholds. This study conducted a series of meta-analyses and compiled results to identify thresholds specific to S&C, and create prior distributions for Bayesian updating. Pre- and post-training data from S&C interventions were translated into standardised mean difference (SMDpre) and percentage improvement (%Improve) effect sizes. Bayesian hierarchical meta-analysis models were conducted to compare effect sizes, develop prior distributions, and estimate 0.25-, 0.5-, and 0.75-quantiles to determine small, medium, and large thresholds, respectively. Data from 643 studies comprising 6574 effect sizes were included in the analyses. Large differences in distributions for both SMDpre and %Improve were identified across outcome domains (strength, power, jump and sprint performance), with analyses of the tails of the distributions indicating potential large overestimations of SMDpre values. Future evaluations of S&C training will be improved using Bayesian approaches featuring the information and priors developed in this study. To facilitate an uptake of Bayesian methods within S&C, an easily accessible tool employing intuitive Bayesian updating was created. It is recommended that the tool and specific thresholds be used instead of isolated effect size calculations and Cohen's generic values when evaluating S&C training.
Related Concept Videos
Regression Toward the Mean
Statistical Significance
Accuracy and Errors in Hypothesis Testing
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
Significance Testing: Overview
Interpretation of Confidence Intervals
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Statistical Hypothesis Testing
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...

