Sample size estimation for biomechanical waveforms: Current practice, recommendations and a comparison to discrete power analysis

Testing a prediction is fundamental to scientific experiments. Where biomechanical experiments involve analysis of 1-Dimensional (waveform) data, sample size estimation should consider both 1D variance and hypothesised effects. This study exemplifies using typical signals contrasts this with 0D (discrete) power analysis. For context, biomechanics papers from 2018 2019 were reviewed characterise current practice. Sample occurred in approximately 4% 653 reporting practice was mixed. To estimate sizes, common sourced the literature effects generated artificially open-source power1d software. Smooth Gaussian noise added modelled effect numerically required. sizes estimated procedures varied according characteristics dataset, requiring only small-to-moderate 5–40 achieve target powers 0.8 for reported effects, but always larger than (from N + 1 >N 20). The importance priori highlighted recommendations are provided improve consistency reporting. enable researchers construct address adequately powered, hypothesis-driven, predictive research questions.