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It is hypothesized that by implanting quarterly mock codes, nurses will feel more competent when their patients deteriorate.

A null hypothesis would be the following: an increase in mock codes will not change how prepared nurses are for codes. This hypothesis assumes there is no association between the two variables (Stunt et al., 2021). The researchers' goal is to reject this and accept the alternative hypothesis.

Alternative Hypothesis: An increase in mock codes will improve competence of nurses during codes.

A type I error could occur if the data suggest an effect of the mock codes when there was not improvement in competence. In this kind of error, it is believed that there was an effect from the independent variable when there was not (Ambrose, 2022). As a result, the researcher wrongfully rejects the null hypothesis.

A type II error could have occurred if the data suggest that there was no improvement in competence in mock codes when in fact there was an improvement. In this kind of error, it is believed that there was no effect from the independent variable when there was (Ambrose, 2022). As a result, the researcher does not reject the null hypothesis.

One- or Two-Tailed Test: This is a one-tailed test because the researchers are predicting an increase in competence after more frequent mock codes.