In this paper, the validity of the stochastic model-based variance distribution of surface electromyogram (EMG) signals during isometric contraction is investigated. In the model, the EMG variance is considered as a random variable following an inverse gamma distribution, thereby allowing the representation of variations in the variance. This inverse gamma-based model for the EMG variance is experimentally validated through comparison with the empirical distribution of variances. The difference between the model distribution and the empirical distribution is quantified using the Kullback- Leibler divergence. Additionally, regression analysis is conducted between the model parameters and the statistics calculated from the empirical distribution of EMG variances. Experimental results showed that the inverse gamma-based model is potentially suitable and that its parameters can be used to evaluate the stochastic properties of the EMG variance.