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Exercise 1

A Doctor of Nursing Practice (DNP) quality improvement project was designed to enhance self-efficacy among adults with Type II diabetes through a self-management education intervention. To evaluate the effectiveness of this intervention, the DNP nurse aims to compare self-efficacy levels before and after the intervention using dependent samples, as the same participants are measured at two time points.

Determining the appropriate sample size is critical to ensure the study has sufficient power to detect statistically significant differences. Using a power analysis with the following parameters = 0.05 (significance level), power = 0.80, and a medium effect size (Cohen's d = 0.5), the required sample size per group is 33 participants. This calculation assumes equal group sizes and dependent samples, which is appropriate given the pre- and post-intervention design.

Power analysis is essential in DNP projects to minimize Type I and Type II errors and to ensure that the study results are both valid and generalizable. A sample size that is too small may fail to detect meaningful changes, while an excessively large sample may waste resources. The use of statistical tools such as G*Power or Statsmodels in Python facilitates accurate sample size estimation.

This approach aligns with best practices in clinical research and supports evidence-based decision-making in nursing. As emphasized by Grove & Cipher (2019) and Sylvia & Terhaar (2018), understanding statistical principles is foundational for translating research into practice.

Exercise 2

A Doctor of Nursing Practice (DNP) quality improvement project was conducted to promote weight loss among obese adult patients in a primary care setting. The intervention included four behavioral strategies: (1) keeping a food diary, (2) practicing meditation, (3) eating without electronic distractions, and (4) using smaller plates. The DNP nurse aimed to determine whether there were statistically significant differences in weight loss among the four groups.

To ensure the study's validity, a power analysis was performed to calculate the necessary sample size. Given the assumptions of a normally distributed variable, = 0.05, power = 0.80, and more than two groups, a one-way ANOVA was selected as the appropriate statistical test. Using the weight loss data collected from each group, the effect size was estimated and used to compute the required sample size.

The analysis revealed that a minimum of 40 participants per group is needed to detect a statistically significant difference in weight loss among the four strategies. This sample size ensures adequate power to minimize Type I and Type II errors and supports the reliability of the findings.

Experiences with Statistics

My experience with statistics has been a mix of challenge and growth. Initially, I found statistics intimidating filled with unfamiliar terminology and complex formulas. Concepts like standard deviation, p-values, and confidence intervals felt abstract and disconnected from clinical practice. However, as I engaged more deeply with research and evidence-based projects, I began to appreciate the power of statistics in shaping meaningful healthcare decisions. The turning point came when I learned to use tools like G*Power and SPSS, which helped me visualize data and understand the rationale behind statistical tests. Collaborating with a statistician during a quality improvement project also boosted my confidence, as I saw how proper analysis could validate our interventions and improve patient outcomes. Success with statistics requires persistence, curiosity, and support. Breaking down concepts into real-world applications, such as comparing patient outcomes across interventions, makes statistics more relatable. Using visual aids, attending workshops, and practicing with sample datasets are also helpful strategies. Most importantly, asking questions and seeking mentorship can transform statistics from a barrier into a bridge toward clinical excellence. Ultimately, statistics is not just about numbersit's about making informed decisions that enhance care, safety, and innovation in nursing practice.

Understanding statistical principles such as effect size and power analysis is essential for DNPs conducting evidence-based interventions. As emphasized by Grove & Cipher (2019) and Sylvia & Terhaar (2018), rigorous statistical planning enhances the credibility and impact of clinical research.

References

Grove, S. K., & Cipher, D. J. (2019). Statistics for nursing research: A workbook for evidence-based practice (3rd ed.). Elsevier

Sylvia, M. L., & Terhaar, M. F. (2018). Clinical analytics and data management for the DNP (2nd ed.). Springer Publishing Company