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Statistics is used in different types of research in order to describe trends and determine relationships between variables. It is possible to agree that, as a result of applying statistical methods, a researcher can analyze and interpret different data sets and respond to the formulated research questions. In this context, a researcher can utilize statistical tests that are appropriate for quantitative, qualitative, and mixed methods studies (Lang & Altman, 2016). The selection of statistical tests and methods of analysis depends on the research questions and purpose, as well as on the type of the collected data.
Various statistical tests selected by researchers can be related to descriptive or inferential statistics. It is important to note that descriptive statistics is used when a researcher needs to describe data, demonstrate tendencies and averages, and summarize collected information. Descriptive statistics is effective to organize data in an efficient way, for instance, while presenting sample characteristics and results of the frequency distribution (Gaskin & Happell, 2014). Such data are often summarized in the form of graphs or tables.
Furthermore, in addition to descriptive statistics, it is also important to use inferential statistics to test hypotheses and examine relationships between the set variables. Researchers can analyze the characteristics of their samples and specifics of their variables, as well as assumptions and predictions reflected in hypotheses, in order to choose the most appropriate statistical tests that will help provide data required for answering research questions (Bettany‐Saltikov & Whittaker, 2014). The examples of such tests include t-tests, chi-square tests, correlation analysis, regression analysis, path analysis, and factor analysis among other types. These tests allow for examining associations or correlations between different phenomena.
References
Bettany‐Saltikov, J., & Whittaker, V. J. (2014). Selecting the most appropriate inferential statistical test for your quantitative research study. Journal of Clinical Nursing, 23(11-12), 1520-1531.
Gaskin, C. J., & Happell, B. (2014). Power, effects, confidence, and significance: An investigation of statistical practices in nursing research. International Journal of Nursing Studies, 51(5), 795-806.
Lang, T., & Altman, D. (2016). Statistical analyses and methods in the published literature: The SAMPL guidelines. Medical Writing, 25, 31-36.
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