Develop And State Your Own Research Hypothesis And Its Cor

Develop And State Your Own Research Hypothesis And Its Cor

Question #3: Develop and state your own research hypothesis and its corresponding two statistical hypotheses [i.e., the alternative hypothesis (H1) and the null hypothesis (H0)]. Describe the relationships between the two statistical hypotheses; the relationship between the alternative hypothesis and the research hypothesis; and state the two possible results after hypothesis testing. How do Type I and Type II errors relate the alternative and null hypotheses?

Paper For Above instruction

Formulating a clear and testable research hypothesis is fundamental in scientific inquiry. A research hypothesis is a specific, directional statement predicting an expected relationship or difference between variables based on theoretical grounding or prior evidence. For illustrative purposes, consider a study examining the effect of a mindfulness training program on reducing stress levels among college students. The research hypothesis (also called the alternative hypothesis) posits that participation in mindfulness training will significantly reduce student stress levels compared to no intervention.

Based on this, the formal statistical hypotheses can be articulated as follows: the null hypothesis (H0) states that there is no difference in stress levels between students who participate in mindfulness training and those who do not; mathematically, H0: μ1 = μ2, where μ1 is the mean stress score of the intervention group, and μ2 is the mean stress score of the control group. Conversely, the alternative hypothesis (H1) asserts that there is a significant difference (specifically, a reduction) in stress levels for students undergoing mindfulness training, expressed as H1: μ1

The relationship between the research hypothesis and these null and alternative hypotheses is direct: the research hypothesis predicts the specific effect or relationship that the alternative hypothesis tests statistically. In this case, the research hypothesis is that mindfulness training reduces stress, which aligns with the alternative hypothesis that μ1

When conducting hypothesis testing, two possible outcomes emerge. The first is rejecting the null hypothesis when it is false, thereby providing support for the research hypothesis. The second is failing to reject the null hypothesis when it is actually false, which may represent a Type II error. Conversely, incorrectly rejecting the true null hypothesis constitutes a Type I error. These errors are central to testing because they determine the credibility and interpretability of the results. A Type I error (α) reflects a false positive, implying that researchers found an effect where none exists, while a Type II error (β) is a false negative, whereby a true effect is overlooked.

In summary, the research hypothesis guides the formulation of the null and alternative hypotheses, with the latter aligning with the predicted effect. The relationships between these hypotheses are foundational to inferential testing. The decision to reject or not reject the null hypothesis involves managing the risks of Type I and Type II errors, which balance the probabilities of false positives and false negatives, respectively. Proper understanding and control of these errors enhance the validity of scientific conclusions.

References

• Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
• Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for the Behavioral Sciences. Cengage Learning.
• Harlow, L. L. (2014). Regression Analysis. In G. R. H. et al. (Eds.), The Sage Dictionary of Statistics. Sage Publications.
• Kaplan, D. (2014). The Conduct of Inquiry: Methodology for Behavioral Science. Psychology Press.
• Moore, D. S., McCabe, G. P., & Craig, B. A. (2017). Introduction to the Practice of Statistics. W.H. Freeman.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Pearson.
• Levitt, M., & List, J. A. (2007). Viewpoint: On the Cytology of Hypotheses and Errors in Science. American Journal of Agricultural Economics, 89(2), 382-399.
• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.
• Wasserstein, R. L., & Lazar, N. A. (2016). The ASA Statement on p-Values: Context, Process, and Purpose. The American Statistician, 70(2), 129-133.
• Yuan, Y., & Maxwell, S. E. (2005). Discrete and Continuous Scores in Structural Equation Modeling. Multivariate Behavioral Research, 40(2), 255-277.