Statistics: Assessing T-tests

Assessing T-tests To clearly identify what a t-test accomplishes in descriptive statistics it is imperative to understand what a t-test represents. A “t-test is a parametric statistical test for comparing the means of two independent samples” (Plichta & Kelvin, 2013, p. 464). Gosset developed the t-test for use in quality control at the Guinness Brewery and published his works under the pen name “Student” (Plichta & Kelvin, 2013). T-tests use assumptions related to the underlying variable of study, where it is assumed that the underlying variable is distributed normally (Fagerland, 2012, p. 76). In essence, the t-test examines differences between two groups on a variable of interest.
Research Study Review
Bradley (2012), proposed to study the effects of nurse practitioners (NP’s) utilizing a skin cancer screening tool along with receiving further education on skin cancer assessment and diagnosis. The study was predicated on the Health Promotion Model (HPM), which was selected based on it relevance to the practice of NP’s and their duty to promote healthy behaviors (Bradley, 2012). “Evidence-based research showcased within the HPM can aid NPs in making practical recommendations to reduce skin cancer risk, specifically melanoma” (Bradley, 2012, p. 83). Study participants (NP’s) were provided with training and educational programs to increase their knowledge and subsequent assessment skills and documentation of physical skin assessments for patients. “The HPM was used in this study to examine the health status and health promotion behaviors of young adults with respect to screening for melanoma” (Bradley, 2012, p. 83). The study was conducted in two phases where “phase one utilized a quasi experimental design to examine characteristics of a single sample of NPs exploring the aspects of skin cancer screening phenomena among them in a single college health center setting” (Bradley, 2012, p. 83). NP’s were given a pre and post test to

References: Bradley, H. B. (2012). The implementation of a skin cancer screening tool in a primary care stetting: A pilot study. Journal of the American Academy of Nurse Practitioners, 24(2), 82-88. http://dx.doi.org/10.1111/j.1745-7599.2011.00669.x DiCalogero, S. (n.d.). Statistical Sampling: A guide for gathering data [PowerPoint slides]. Retrieved from Tidewater Community College: www.tcc.edu/vml/documents/StatisticalSampling.pptx_ Fagerland, M. W. (2012). T-tests, non-parametric tests, and large studies--a paradox of statistical practice? BMC Medical Research Methodology, 12(1), 76-83. http://dx.doi.org/10.1186/1471-2288-12-78 Heavey, E. (2011). Statistics for nursing. Sudbury: MA: Jones and Bartlett Learning. Kaplan University. (n.d.). Overview. Retrieved from http://kucourses.com/re/DotNextLaunch.asp?courseid=8514503 Levy, Y., & Ellis, T. J. (2011). A guide for novice researchers on experimental and quasi-experimental studies in information systems research. Interdisciplinary Journal of Information, Knowledge, and Management , 6, 151-161. North Carolina State University. (n.d.). Independent Variable. Retrieved from http://www.ncsu.edu/labwrite/po/independentvar.htm O’Rourke, N., Hatcher, L., & Stepanksi, E. (2005). A step by step approach to using SAS for univariate and multivariate statistics (2nd ed.). Cary, NC: SAS Institute. Plichta, S. B., & Kelvin, E. A. (2013). Munro’s statistical methods for health care research (6th ed.). Philadelphia, PA: Wolters Kluwer Health/Lippincott, Williams & Wilkins. University of West Florida. (n.d.). Scales of measurement. Retrieved from uwf.edu/pcl/research/edf6481/week04/files/ScalesofMeasurement.rtf