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Pataky, T, Vanrenterghem, J and Robinson, MA (2016) The probability of false positives in zero-dimensional analyses of one-dimensional kinematic, force and EMG trajectories. Journal of Biomechanics. ISSN 1873-2380
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Abstract
A false positive is the mistake of inferring an e↵ect when none exists, and although alpha controls the false positive (Type I error) rate in classical hypothesis testing, a given alpha value is accurate only if the underlying model of randomness appropriately reflects experimentally observed variance. Hypotheses pertaining to one-dimensional (1D) (e.g. time-varying) biomechanical trajectories are most often tested using a traditional zero-dimensional (0D) Gaussian model of randomness, but variance in these datasets variance is clearly 1D. The purpose of this study was to determine the likelihood that analyzing smooth 1D data with a 0D model of variance will produce false positives. We first used random field theory (RFT) to predict the probability of false positives in 0D analyses. We then validated RFT predictions via numerical simulations of smooth Gaussian 1D trajectories. Results showed that, across a range of public kinematic, force and EMG datasets, the median false positive rate was 0.382 and not the assumed alpha=0.05, even for a simple two-sample t test involving N=10 trajectories per group. The median false positive rates for experiments involving three-component vector trajectories was p=0.764. This rate increased to p=0.945 for two three-component vector trajectories, and to p=0.999 for six three-component vectors. This implies that experiments involving vector trajectories have a high probability of yielding 0D statistical significance when there is, in fact, no 1D effect. Either (a) explicit a priori identification of 0D metrics or (b) adoption of 1D methods can more tightly control alpha.
| Item Type: | Article |
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| Uncontrolled Keywords: | 0903 Biomedical Engineering, 1106 Human Movement And Sports Science, 0913 Mechanical Engineering |
| Subjects: | R Medicine > RC Internal medicine > RC1200 Sports Medicine |
| Divisions: | Sport & Exercise Sciences |
| Publisher: | Elsevier |
| Date Deposited: | 18 Mar 2016 12:23 |
| Last Modified: | 04 Sep 2021 13:09 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/3291 |
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