The t-test is arguably one of the most popular statistical procedures used in hypothesis testing in qualitative and quantitative research. According to Kim (2015), a hypothesis refers to general statements or claims made about a population that are proven through the use of statistical measures. Researchers select one of the three types of t-tests, which include two-tailed, upper-tailed, and lower-tailed, depending on the study methodology. There is a significant difference in conducting two-tailed, upper-tailed, and lower-tailed t-tests; in spite of their application in quantitative and qualitative research.
The direction is the primary difference between the two-tailed, upper-tailed, and lower-tailed t-tests. Both the lower-tailed and upper-tailed t-tests are directional meaning that the critical area used in the hypothesis testing is one-sided, and the relationship in the other direction is ignored (Holcomb, & Cox, 2017). When using the lower-tailed t-test, the calculated test static is compared to the critical in the negative or lower side of the distribution curve. For the upper-tailed test, the calculated test statistic is compared to the significant value on the positive or upper half of the distribution curve to confirm the null hypothesis. Conversely, the two-tailed t-test is bi-directional; meaning that the relationship of the test-statistic can either be greater or lesser than the critical value on either side of the distribution curve (Holcomb, & Cox, 2017). For all the three tests, the alternative hypothesis is accepted when the calculated test statistic falls in the critical region.
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There are different examples of the use of t-tests in quantitative and qualitative research. For example, the two-tailed t-test is used in the qualitative pain studies to determine the threshold of pain within which different medications can be used. On the other hand, upper-tailed t-test is commonly used in obesity research to determine the number of people in the population that are overweight or obese. Therefore, the t-test is an essential statistical measure.
References
Holcomb, Z. C., & Cox, K. S. (2017). Interpreting Basic Statistics: A Workbook Based on Excerpts from Journal Articles . Routledge.
Kim, T. K. (2015). T-test as a parametric statistic. Korean journal of anesthesiology , 68 (6), 540.
