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Quantitative Research Methods: Selecting and Interpreting Inferential Statistics Discussion Board #5 Reply Kaleah L. Singleton May 07, 2023 Author Note Kaleah L. Singleton I have no known conflict of interest to disclose. Correspondence concerning this article should be addressed to Kaleah L. Singleton Email: klblden@liberty.edu

Discussion Thread: Selecting and Interpreting Inferential Statistics Summary Lynn, you did a great job summarizing the importance of a one-sample t test. The primary purpose of this test is to look at the statistical significance between a mean or assumed value (Morgan et al., 2020). For the t test to work properly, the dependent variables should be consistent. T-tests are useful when looking to compare norms, as seen in the HSB data set. The researcher is able to determine if there is a difference in the norms and if it is significant (Morgan et al., 2020). When interpreting the differences for the means of grades in high school and visualization, you mentioned that there is a significant difference specific to math achievement scores between the two groups. There was not a significant difference when comparing the groups specific to high school grades. The visualization test did show a significant difference (Morgan et al., 2020). Mann-Whitney U test is most useful when looking to analyze the difference between two separate groups specific to a dependent variable. Researchers use this test when data is not normally distributed and when equal variances are broken (Morgan et al., 2020). Agree/Disagree I agree that Wilcoxon tests are most useful when looking to compare pairs within a data set . For example, if you were looking at one set of participants but analyzing their scores on two different skills tests (Morgan et al., 2020). One additional point to add is that this test ranks the data sets. This is another way to analyze the data and how the pairs compare to each other.

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