A STATISTICAL APPROACH FOR RELIABLE MEASUREMENT WITH FAMILIARIZATION TRIALS

An accurate and reliable measurement is important in exercise science. The measurement tends to be less reliable when subjects are not professional athletes or are unfamiliar with a given task. These subjects need familiarization trials, but determination of the number of familiarization trials is challenging because it may be individual-specific and task-specific. Some participants may be eliminated because their results deviate from arbitrary ad hoc rules. We treat these challenges as a statistical problem, and we propose model-averaging to measure a subject’s familiarized performance without fixing the number of familiarization trials in advance. The method of model-averaging accounts for the uncertainty associated with the number of familiarization trials that a subject needs. Simulations show that model-averaging is useful when the familiarization phase is long or when the familiarization occurs at a fast rate relative to the amount of noise in the data. An applet is provided on the internet with a very brief User’s Guide included in the appendix to this article. Keywords: Familiarization; reliability; accuracy; model-averaging; Akaike Information Criterion