Statistics: Assessing Non-Parametrics or T-Tests

Introduction: The Use of T-Tests in Nutrition Studies

Used to find out whether there is a difference between two groups of numbers, a t-test is an efficient means to conduct a mathematical analysis (Keller, 2011). Obviously useful in a number of fields, a t-test is essential for the evaluation of a specific issue. Therefore, when it comes to people’s health and the development of healthy habits, research based on t-tests can offer adequate data.

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Concerning the Statistics: Evaluating the Research Data

In the chosen article, i.e., Change in diet, physical activity, and body weight among young adults during the transition from high school to college by Wengreen & Moncur (2009), the method of the t-test is used to evaluate the weight change in an experimental group of students.

The Study and Its Specifics: Nutrition as a Crucial Process

In the study by Wengreen & Moncur (2009), the statistical data concerning the students’ weight and the weight differentials within the group in the course of diet change was used.

The Use of Statistics in the Study: A Cautious Attempt at Generalizing

The statistics were used in the study to show how the change in eating habits can affect a student’s weight. Displaying the minimum and maximum weight among students, as well as helping calculate the average student weight in the group, the researchers operate the statistical data.

Questioning the Reasonability of the T-Tests: The Choice of Methodology

The application of the t-test is justified by the fact that the research features two groups and two different dieting approaches. Therefore, specifying the difference between the two groups after the effects of nutrition have become evident is important (Mitchell & Jolley, 2012).

Meeting the Tests Assumptions: the Hypothesis Proves Right

It is important to mention that the use of the t-test partially helps prove the initial research hypothesis. Although the authors state that they “cannot determine whether the observed increases in body weight were associated with growth or increases in lean or non-lean body mass” (Wengreen & Moncur, 2009, 82), it is still clear that the change in the diet and, therefore, weight among students is greatly influenced by the change in setting, particularly due to the transition from high school to college.

Evaluating the Means of Data Display: Graphs and Tables

The data is displayed fairly decently in the research. Placed in tables, it allows to compare and contrast the differences between the experimental groups and, thus, form an opinion concerning the dieting issues among the high school students.

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The Strengths and Weaknesses of the Research: What Could Have Been Improved

The key strength of the research is the detailed analysis of the information and the high precision of the weight data. The downside is, however, that the research has its limitations, especially concerning the number of participants and the impossibility to take into account the specifics of each student’s metabolic process.

Conclusion: When the Difference Between the Two Groups Is Crucial

As the analysis of the t-tests application in a specific research (in Wengreen and Moncur’s Change in diet) shows, t-tests provide accurate research data and allow to figure out whether the difference between the research groups is actually accidental, or whether there are considerable reasons to believe that the difference in the research results shows the inefficiency of the chosen dieting method. Once applying the t-test in quantitative or mixed research, one can expect accurate results and high rates of precision.

Reference List

Keller, G. (2011). Statistics for management and economics. Stamford, CT: Cengage Learning.

Mitchell, M. L. & Jolley, J. M. (2012). Research design explained. Stamford, CT: Cengage Learning.

Wengreen, H. J. & Moncur, C. (2009). Change in diet, physical activity, and body weight among young-adults during the transition from high school to college. Nutrition Journal, 22(8), 32–37.

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StudyCorgi. (2020) 'Statistics: Assessing Non-Parametrics or T-Tests'. 10 May.

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