T Tests And ANOVA In Clinical Practice
t Tests and ANOVA in Clinical Practice
Inferential statistics enable researchers to apply the data they gather and the conclusions they draw from a particular sample to a larger population. As the name implies, inferential statistics focus on inferring whether there is a relationship between two or more variables. These statistical analyses include t tests and analysis of variance (ANOVA). t Tests are part of a group of statistical tests that test hypotheses; in fact, it is necessary to formulate a hypothesis in order to use a t test, because the results of the test can only be interpreted in the context of a scientific hypothesis.
Inferential statistics such as t tests work well for comparing two groups. Although mathematically equivalent to the t test, ANOVA allows for the comparison of more than two groups. Therefore, when three or more groups are involved, the ANOVA should be used.
In this week’s Discussion, you are asked to locate a current research article that utilizes either a t
test or ANOVA analysis. You provide a summary of the research study and of the study’s application to evidence-based practice. You also examine the article’s use of a t test or ANOVA and how either of those statistical analysis tools helped to inform the article’s conclusions and recommendations.
Consider some of the important issues in health care delivery or nursing practice today. Bring to mind the topics to which you have been exposed through previous courses in your program of study, as well as any news items that have caught your attention recently. Select one topic to consider for this Discussion.
Next, review journal, newspaper, and Internet articles that provide credible information on your topic (you can choose any nursing topic from Confidentiality or Work Place Bullying or any other nursing related issue that will be easy to locate a scholarly research article on which uses a t test or ANOVA)
Then, select one research article on which to focus that used inferential statistical analysis (either a t test or ANOVA) to study the topic.
With information from the Learning Resources in mind, evaluate the purpose and value of the research study discussed in your selected article and consider the following questions:
Who comprised the sample in this study?
What were the sources of data?
What inferential statistic was used to analyze the data collected (t test or ANOVA)?
What were the findings?
Ask yourself: How did using an inferential statistic bring value to the research study? Did it increase the study’s application to evidence-based practice?
By tomorrow Wednesday 09/27/17, 8 pm, write a minimum of 550 words essay in APA format with a minimum of 3 references from the list in the instructions area. Include the level one headings as numbered below:
Post a cohesive response that addresses the following:
1) Identify the topic you selected in the first line of your posting. (you can choose any nursing topic from Confidentiality or Work Place Bullying or any other nursing related issue that will be easy to locate a scholarly research article on which uses a t test or ANOVA)
2) Summarize the study discussed in your selected research article and provide a complete APA citation. Include in your summary the sample, data sources, inferential statistic utilized, and findings.
3) Evaluate the purpose and value of this particular research study to the topic.
4) Did using inferential statistics strengthen or weaken the study’s application to evidence-based practice?
Gray, J.R., Grove, S.K., & Sutherland, S. (2017). Burns, and Grove’s the practice of nursing research: Appraisal, synthesis, and generation of evidence (8th ed.). St. Louis, MO: Saunders Elsevier.
Chapter 25, “Using Statistics to Determine Differences”
This excerpt elaborates on how statistics are used to examine causality using procedures such as contingency tables, chi-squares, t tests, and analysis of variance (ANOVA).
Statistics and Data Analysis for Nursing Research
Chapter 5, “Statistical Inference”
This chapter discusses inferential statistics, sampling error, sampling distributions, and the laws of probability. The chapter also introduces key terms such as standard error of mean, hypothesis testing, and parametric test.
Chapter 6, “t Tests: Testing Two Mean Differences”
This chapter considers the various forms of the t test, including the two-sample t test, Kolmogrov-Smirnov test, independent groups t test, and dependent groups t test. The chapter also discusses the many variables involved in these tests such as effect size, meta-analysis, and Cohen’s d.
Chapter 7, “Analysis of Variance” (pp. 137–146 and 155–158)
The first part of this chapter introduces the basic assumptions, requirements, general logic, and terminology surrounding analysis of variance (ANOVA). The second excerpt focuses on sampling distribution of the F ratio and the null and alternative hypotheses.