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In statistical hypothesis testing, model misspecification error occurs when the real model of the data is none of the models under null and alternative hypotheses. This research has studied the probability of model misspecification errors in one-sided tests. These error rates are compared between the Neyman-Pearson and evidential statistical inference approaches. The results show that the evidential approach works better than the Neyman-Pearson approach.