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ChatGPT for Sample-Size Calculation in Sports Medicine and Exercise Sciences: A Cautionary Note

Jabeur Methnani, Imed Latiri, Ismail Dergaa, Karim Chamari, and Helmi Ben Saad

Therefore, these high rates of significant findings in light of the insufficiently powered studies raise concern on questionable research practices and publication bias in our field. 5 One approach to address this issue is to a priori estimate sample size. 6 Indeed, this was a major indication of a 2022

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Data Analyses When Sample Sizes Are Small: Modern Advances for Dealing With Outliers, Skewed Distributions, and Heteroscedasticity

Rand Wilcox, Travis J. Peterson, and Jill L. McNitt-Gray

relatively large sample sizes (n > 200), classic methods can yield inaccurate confidence intervals and can have relatively poor power. 6 , 7 In practical terms, if Student’s t test fails to reject, one possibility is that there is little or no difference between the groups, or it might be because power is

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The N-Pact Factor, Replication, Power, and Quantitative Research in Adapted Physical Activity Quarterly

Jeffrey Martin and Drew Martin

.e., the p value). All things being equal (e.g., similar measurement error), a study with a large sample size will be more adequately powered than one with a smaller sample size. As a result, research with large samples is more likely to discover small effects that are significant and may be quite

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Determinants of Variance in the Habitual Physical Activity of Overweight Adults

David R. Paul, Matthew Kramer, Kim S. Stote, and David J. Baer

Background:

The number of days of data and number of subjects necessary to estimate total physical activity (TPA) and moderate-to-vigorous physical activity (MVPA) requires an understanding of within- and between-subject variances, and the influence of sex, body composition, and age.

Methods:

Seventy-one adults wore accelerometers for 7-day intervals over 6 consecutive months.

Results:

Body fat and sex influenced TPA and MVPA. The sources of subject-related variation for TPA and MVPA were within-subject (48.4% and 54.3%), between-subject (34.3% and 31.8%), and calendar effects (17.3% and 13.9%). Based on within-subject variances, the error associated with estimating TPA and MVPA by collecting 1 to 7 days of data ranged from 28.2% to 13.3% for TPA and 62.0% to 28.6% for MVPA. Based on between-subject variances, detecting a 10% difference between 2 groups at a power of 90% requires approximately 200 and 725 subjects per group for TPA and MVPA, respectively.

Conclusions:

Estimates of MVPA are more variable than TPA in overweight adults, therefore more days of data are required to estimate MVPA and larger sample sizes to detect treatment differences for MVPA. Log-transforming data reduces the need for additional days of data collection, thereby improving chances of detecting treatment effects.

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The Numbers Will Love You Back in Return—I Promise

Martin Buchheit

The first sport-science-oriented and comprehensive paper on magnitude-based inferences (MBI) was published 10 y ago in the first issue of this journal. While debate continues, MBI is today well established in sport science and in other fields, particularly clinical medicine, where practical/clinical significance often takes priority over statistical significance. In this commentary, some reasons why both academics and sport scientists should abandon null-hypothesis significance testing and embrace MBI are reviewed. Apparent limitations and future areas of research are also discussed. The following arguments are presented: P values and, in turn, study conclusions are sample-size dependent, irrespective of the size of the effect; significance does not inform on magnitude of effects, yet magnitude is what matters the most; MBI allows authors to be honest with their sample size and better acknowledge trivial effects; the examination of magnitudes per se helps provide better research questions; MBI can be applied to assess changes in individuals; MBI improves data visualization; and MBI is supported by spreadsheets freely available on the Internet. Finally, recommendations to define the smallest important effect and improve the presentation of standardized effects are presented.

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Large N: A Strategy for Improving Regional Sport Performance

Andrew C. Cornett and Joel M. Stager

It has been hypothesized that large differences in maximal performance can arise between various geopolitical regions solely on the basis of differing numbers of participants in the target activity. While there is evidence in support of this hypothesis for a measure of intellectual performance, the same relationship has not been examined for a measure of physical performance.

Purpose:

To determine whether the number of participants is a predictor of the best athletic performance in a region.

Methods:

The 2005–2010 USA Swimming Age Group Detail reports were used to determine the number of competitive swimmers participating in each age group for the 59 local swimming communities in the United States. The USA Swimming performance database provided 50-yd-freestyle times in each community for boys and girls for each age (6–19 y). Simple linear regression was used to examine the relationship between the outcome variable (fastest time) and the predictor variable (log of the number of swimmers) for each combination of age, sex, and calendar year.

Results:

The log of the number of swimmers in a region was a significant predictor of the best performance in that region for all 168 combinations of age, sex, and calendar year (P < .05) and explained, on average, 41%, and as much as 62%, of the variance in the fastest time.

Conclusions:

These findings have important implications for the development of regional sport strategic policy. Increasing the number of participants in the target activity appears a viable strategy for improving regional performance.

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Comparing Methods for Using Invalid Days in Accelerometer Data to Improve Physical Activity Measurement

Hotaka Maeda, Chris C. Cho, Young Cho, and Scott J. Strath

, Lauderdale, & Waite, 2017 ). Drops in PA measurement validity can result in loss of power for statistical analyses and unstable study results. In addition, the loss of sample size is equally concerning for loss of statistical power as well as biasing the study sample to compliant participants. Therefore

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Alexithymia and the Anxiolytic Effect of Endurance Running

Tim Woodman and Charlotte Welch

). Manfredi and Gambarini ( 2015 ) found that 100% of exercise addicted participants ( n  = 12) were alexithymic. Despite the clear limitation of the small sample size, this finding is further supported in the sparse literature assessing this topic. For example, Bossard and Miller ( 2009 ) assessed the

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Effectiveness of Outdoor Exercise Parks on Health Outcomes in Older Adults—A Mixed-Methods Systematic Review and Meta-Analysis

Yoke Leng Ng, Keith D. Hill, Pazit Levinger, and Elissa Burton

Blood Institute quality assessment tool assessed quality with 14 questions on research objective, study population, participation rate, method of recruitment, sample size, outcome(s), timeframe, levels and assessment of exposure, outcome measures, blinding of assessors, dropout rate, and statistical

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On the Reproducibility of Power Analyses in Motor Behavior Research

Brad McKay, Mariane F.B. Bacelar, and Michael J. Carter

In statistics, power is the probability of observing a significant effect given the statistical analysis, sample size, and the true effect size in the population. Recent evidence suggests that many studies in sports science and motor behavior have been underpowered to reliably detect the effects