2.4. Statistical Analysis
Means and standard deviations were used to describe participant data. The primary factors in the statistical models were condition and sex. The two levels of the condition variable were fasting alone and fasting with exercise. The sample size was calculated a priori. Using an alpha of 0.05, a beta of 0.20, and an effect size of 0.66 (a ten percent difference between conditions) resulted in a sample of 20 participants.A mixed-model repeated-measures ANOVA was used to evaluate the time course differences for each hormone over the 36-h fast for each condition, controlling for baseline values. Where there were significant interactions between time and condition, a post hoc least significant difference pairwise comparison was used to examine the differences. Gender was included in the model as a covariate, and the interactive affect between gender and condition was evaluated. The area under the treatment curve was calculated using the trapezoidal rule for the plasma concentration of each hormone. The area under the curve was used to better represent the total response of each variable. A mixed-model repeated-measures ANOVA was used to compare areas under the curve for each condition. The cut point for statistical significance was 