## Problem Statement:

While senior-level nursing students in ADN programs are crucial contributors to healthcare, there exists a need to enhance their resuscitation self-efficacy. Recognizing the significance of statistical analysis assignment simulation training, this study addresses the question of whether the introduction of a 'code blue' scenario has a statistically significant impact on Resuscitation Self-efficacy Scale scores. By delving into the intricacies of sample size determination, statistical power calculation, and strategies for handling non-normally distributed data, the research aims to provide valuable insights for refining educational approaches and improving the preparedness of future nursing professionals.

### Sample Size and Statistical Power Determination:

In addressing the critical aspect of sample size, Chander (2017) emphasizes the need for a scientifically obtained and optimum sample size for valid study results. Utilizing G*Power software, the study calculates a minimum sample size of 106 participants, employing the Mann-Whitney U test with a medium effect size of 0.5 and a significance level of 0.05. This ensures the study's statistical power at 80%.

### Handling Non-Normal Data Distribution:

Recognizing the importance of addressing non-normal data distribution, the study opts for the Wilcoxon signed-rank test, a non-parametric approach, when the assumption of normality is violated. This method provides robustness in analyzing paired samples without relying on the regular distribution of data, offering better statistical power than the paired t-test under various circumstances.

### Threats to Internal and External Validity:

The study considers potential threats to internal validity such as maturation effects, testing sensitization, and regression towards the mean. To mitigate these, the post-test is administered immediately after the scenario simulation, preventing sensitization to the post-test due to pretest exposure. Additionally, the study acknowledges the challenge of regression towards the mean, emphasizing the need for careful evaluation to distinguish genuine change from statistical phenomena.

Addressing external validity concerns, the study employs a Proxy who is unfamiliar with the student cohort to prevent bias in settings and organizations. High-fidelity simulation is used to enhance ecological validity, mimicking real-life resuscitation scenarios. This approach ensures that the study's findings can be meaningfully applied to real-world contexts, supporting policy recommendations grounded in both theory and empirical data.

By addressing these key elements, the study aims to contribute valuable insights into improving resuscitation self-efficacy among senior-level ADN nursing students through targeted simulation experiences.