Nr439 Week 6: Data Results and Analysis
This Nr439 Week 6: Data Results graded discussion topic relates to the following Course Outcomes (COs).
- CO1 Examine the sources of evidence that contribute to professional nursing practice. (PO 7)
- CO2 Apply research principles to the interpretation of the content of published research studies. (POs 4 and 8)
After the data are collected, it is time to analyze the results!
- Discuss one of the four basic rules for understanding results in a research study.
- Compare clinical significance and statistical significance. Which one is more meaningful when considering applying evidence to your practice?
- Compare descriptive statistics and inferential statistics in research. Please give an example of each type that could be collected in a study that would be done on your nursing clinical issue you identified in previous weeks.
Nr439 Week 6: Data Results Answer
Understanding the purpose of a study is fundamental for interpreting its results effectively. Mertler and Reinhart (2016) emphasize that the purpose of a study serves as a guiding principle for its methodology and execution. Without a clear comprehension of the research purpose, interpreting study results becomes a challenging task (Houser, 2018).Nr439 Week 6: Data Results
One of the key aspects to consider when interpreting study results is statistical significance. Statistical significance is a quantitative exploration of whether observed differences between groups in a given population are likely due to chance or represent a true effect. Main and Ogaz (2016) define statistical significance through the p-value, indicating a value equal to or less than 0.05. When the p-value is below this threshold, there is substantial evidence against the null hypothesis.Nr439 Week 6: Data Results
In essence, statistical significance provides researchers with a tool to discern whether the observed differences are meaningful or merely the result of random variation. A p-value of 0.05 or less suggests that the findings are unlikely to be due to chance alone, signaling a statistically significant result (Main & Ogaz, 2016).Nr439 Week 6: Data Results
However, it is crucial to recognize that statistical significance does not guarantee the practical or clinical significance of the findings. While a result may be statistically significant, it might not have a substantial impact in real-world scenarios. Effect size, as highlighted by Fritz, Morris, and Richler (2012), becomes imperative in this context. Effect size gauges the magnitude of the observed differences and provides insights into their practical significance. Researchers must, therefore, consider not only statistical significance but also effect size to ascertain the true relevance of the study findings.Nr439 Week 6: Data Results
Moreover, statistical significance is contingent on the sample size. Larger sample sizes increase the likelihood of detecting statistically significant differences, but this does not necessarily translate to enhanced practical significance. Researchers need to strike a balance, considering both statistical and practical significance, to derive meaningful interpretations from their studies (Fritz et al., 2012).
In conclusion, understanding the purpose of a study and recognizing the nuances of statistical significance are essential components of result interpretation in research. Researchers should be mindful that statistical significance does not automatically equate to practical significance. The consideration of effect size and an awareness of the impact of sample size contribute to a more comprehensive and nuanced interpretation of study results. By embracing these principles, researchers can ensure that their findings are not only statistically robust but also possess real-world relevance, advancing the understanding of the phenomena under investigation.
