Nursing Research: Reading Using, and Creating Evidence

Posted on: 10th May 2023



Discussions are designed to promote dialogue between faculty and students, and students and their peers. In discussions students:

Demonstrate understanding of concepts for the week

Integrate outside scholarly sources when required

Engage in meaningful dialogue with classmates and/or instructor

Express opinions clearly and logically, in a professional manner

Use the rubric on this page as you compose your answers.

Best Practices include:

Participation early in the week is encouraged to stimulate meaningful discussion among classmates and instructor.

Enter the discussion often during the week to read and learn from posts.

Select different classmates for your reply each week.

Discussion Questions

Data analysis is key for discovering credible findings from implementing nursing studies. Discussion and conclusions can be made about the meaning of the findings from the data analysis.

Share what you learned about descriptive analysis (statistics), inferential analysis (statistics), and qualitative analysis of data; include something that you learned that was interesting to you and your thoughts on why data analysis is necessary for discovering credible findings for nursing.

Compare clinical significance and statistical significance; include which one is more meaningful to you when considering application of findings to nursing practice.

Title: Nursing Research: Reading, Using and Creating Evidence

Edition: 4

Authors: Janet Houser

Publisher: Jones & Bartlett Learning

APA Citation

Houser, J. (2018). Nursing research: Reading, using, and creating evidence (4th ed.). Jones & Bartlett.BOOK :

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Nursing Research: Reading Using, and Creating Evidence

An inferential analysis is the generalization of the results obtained from the representative samples to correspond to the population from which the sample was drawn (Wyllys, 2018). In most instances, inferential is needed if the sample is randomly obtained with a promisingly high response rate. A medical example of inferential analysis concludes that drug abuse is common among the youth after randomly identified youths proved to be drug abusers.

Descriptive analysis is a variety of analyses that describe a given data and summarize the identified data strategically to reveal the trends that may emerge from the data to meet the data requirements (Bhat, 2019). A medical example of descriptive analysis is identifying that all the participants are not affected in given research about cancer.

Qualitative data analysis identifies and interprets identified patterns and trends in textual data to identify their relationship with the issue of concern (Bhat, 2019). A medical example of qualitative data analysis is the observed shingles in patients affected with "herpes zoster".

Lesson learned on the importance of data analysis.

Data analysis is significant in determining credible findings as it helps avoid biases by statistical treatment assistance (Michael, 2020). In research, correct findings are deterred by bias identification, and therefore a correct analysis prevents biases that translate to accurate findings.

Statistical significance refers to the realness or the faultiness of the differences within the study groups. Clinical significance is a term used to refer to a course where the provided treatment had significant effects and genuine effects. Statistical significance is mainly assigned to naturally occurring results rather than those that occur by chance (Matt, 2018). Clinical significance largely depends on statistical significance due to the computation of the p-values and the significance level. Clinical significance is more meaningful when considering findings in the nursing practice application.



Bhat, A. (2019, June 25). Data analysis in research: Why data, types of data, data analysis in qualitative and quantitative research | QuestionPro. QuestionPro.

Matt. (2018). Clinical Significance vs. Statistical Significance - Side-by-Side Comparison. Mhaonline.

Michael. (2020, October 19). Importance of Data Analysis in experimentation paraphrase Calculator.

Wyllys, R. E. (2018). Teaching descriptive and inferential statistics in library schools. Journal of Education for Librarianship, 3-20.

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