Download The Appropriate File From Student Resources Module

Download the Appropriate File From Student Resources Module 2 Homewo

Download the appropriate file from Student Resources > Module 2 Homework - Module 2 File 1. Generate a research question that could be answered by a single sample t test based on this data. Create a set of hypotheses (alternative and null) for your test. Run a test for skewness and kurtosis to determine if your data is roughly normally distributed. Then perform a single sample t test using the Test Value as specified in the Module 2 Announcement.

Save your output file and post it in this discussion. Post your research question, along with an analysis of your normality tests, a statement of your alternative and null hypotheses for your t test, and your conclusion regarding rejecting or not rejecting the null hypothesis based on your t test, as well as some analysis of the practical significance of the results by 11:59 PM EST/EDT Monday of Residency Week.

Paper For Above instruction

The task involves analyzing a dataset provided in Module 2 Homework - Module 2 File 1, aiming to formulate an appropriate research question suitable for a single sample t test. This statistical test compares a sample mean to a known or hypothesized population mean (Test Value), enabling researchers to determine whether the sample differs significantly from the hypothesized value. The first step is to understand the data by examining its distributional properties, which is essential for validating the assumptions underlying the t test.

To assess whether the data can be considered approximately normally distributed, skewness and kurtosis tests are employed. Skewness measures the asymmetry of the data distribution, with values near zero indicating symmetry, whereas kurtosis assesses the "tailedness" or the propensity for outliers relative to a normal distribution. Conducting these tests provides critical insight into the suitability of applying a t test, which assumes approximate normality, especially with small sample sizes.

Based on the results of skewness and kurtosis tests, the researcher can determine the normality of the data. If the data appears roughly normal, proceeding with a single sample t test is justified; if not, alternative non-parametric methods may be considered. The null hypothesis (H₀) in this context posits that the population mean equals the specified Test Value, indicating no difference between the sample mean and the hypothesized population mean. Conversely, the alternative hypothesis (H₁) suggests that there is a significant difference, either in a two-tailed test or directional if specified.

The single sample t test is then performed using the Test Value provided in the Module 2 Announcement. This involves calculating the t statistic based on the sample mean, the Test Value, the standard deviation, and the sample size. The resulting p-value determines whether the null hypothesis should be rejected at a specified significance level, often 0.05.

After conducting the test, the researcher must interpret the results. If the p-value is less than the significance level, the null hypothesis is rejected, indicating a statistically significant difference between the sample mean and the Test Value. If not, the null hypothesis is retained, implying insufficient evidence to conclude a difference. It is equally important to consider the practical significance of the findings—whether the observed difference is meaningful in a real-world context, rather than solely statistically significant.

Finally, the output of all analyses—including the normality tests, hypotheses, t-test results, and interpretation—should be saved and posted for discussion. This process demonstrates proper application of statistical procedures and critical evaluation of the results in line with the coursework requirements.

References

• Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
• Ghasemi, A., & Zahediasl, S. (2012). Normality Tests for Statistical Analysis: A Guide for Non-Statisticians. International Journal of Endocrinology and Metabolism, 10(2), 486–489.
• Kim, T. (2015). An Introduction to Statistical Learning. Springer.
• Levin, K. A. (2006). Study design III: Cross-sectional studies. Evidence-Based Dentistry, 7(1), 24–25.
• Moore, D. S., & McCabe, G. P. (2013). Introduction to the Practice of Statistics. W. H. Freeman and Company.
• Laerd Statistics. (2018). Shapiro-Wilk Test for Normality using SPSS Statistics. https://statistics.laerd.com/
• Field, A. (2017). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Pearson Education.
• Wilkinson, L., & Task Force on Statistical Inference. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54(8), 594–604.
• Zimmerman, D. W. (2012). Normality Tests: A Guide for the Non-Statisticians. Journal of Modern Applied Statistical Methods, 11(2), 24–43.