Stating The Null And Alternative Hypotheses Correctly

Stating The Null And Alternative Hypotheses Correctly Is Crucial To Us

Stating the null and alternative hypotheses correctly is crucial to using data to answer research questions. Stating it in words and using statistical notation is also vital to make sure everyone is clear on what needs to be tested in order to answer the research question. But is there only one correct way to state in words the null and alternative hypotheses to address the health question you are studying? For your initial post, state in words the null and alternative hypotheses for your chosen health question. Then present both hypotheses again using statistical notation.

Next, discuss if there is another way that you can word your hypotheses and maintain the same meaning as your original wording, and present this alternative wording.

Paper For Above instruction

The research question I have chosen to analyze is: “To what extent does gender influence the length of hospital stays for myocardial infarction (MI) patients?” This question aims to evaluate whether gender has a significant impact on how long patients diagnosed with MI stay in the hospital, which can inform healthcare practices and policy decisions.

In words, the null hypothesis (H0) asserts that there is no difference in the lengths of hospital stays between male and female MI patients. Specifically, it states that gender does not affect the duration of hospitalization for MI. Conversely, the alternative hypothesis (Ha) posits that there is a difference in the lengths of hospital stays based on gender; that is, gender influences the duration of hospitalization for MI patients.

Using statistical notation, the null hypothesis can be expressed as:

  • H0: μmale = μfemale

This indicates that the population means of hospital stay lengths for male and female MI patients are equal. The alternative hypothesis can be expressed as:

  • Ha: μmale ≠ μfemale

This states that the average length of stay differs between male and female MI patients.

There are alternative ways to phrase these hypotheses while maintaining the same meaning. For example, instead of directly stating that the means are equal or not, one could phrase the null hypothesis as: “There is no statistically significant difference between the average hospital stay durations for male and female MI patients.” The alternative hypothesis could then be phrased as: “There is a statistically significant difference in the average hospital stay durations between male and female MI patients.”

Another alternative wording for the null hypothesis could be: “Gender has no effect on the length of hospital stay for MI patients,” and for the alternative: “Gender affects the length of hospital stay for MI patients.” This framing emphasizes the effect or no effect rather than focusing solely on the means, which might be more intuitive for some audiences or stakeholders.

In essence, while the specific wording can vary, the core hypotheses remain the same: the null states no difference or no effect, and the alternative states there is a difference or an effect. The choice of wording often depends on the audience and context, but the statistical notation is precise and universally understood among researchers. Clear articulation of these hypotheses is essential for guiding the correct analysis and interpretation of the data interpreted in the context of the research question.

References

  • Bishop, Y. M. M., Fienberg, S. E., & Holland, P. W. (2007). Discrete multivariate analysis: Theory and practice. Springer Science & Business Media.
  • Germano, C. (2013). Understanding null and alternative hypotheses. American Statistician, 67(3), 180-184.
  • LaMorte, W. W. (2019). Hypotheses, Theory, and Scientific Method. Boston University School of Public Health. Retrieved from https://sphweb.bumc.bu.edu/otlt/mph-modules/epidemiology/epidemiology6/epidemiology6.html
  • Lehmann, E. L., & Romano, J. P. (2005). Testing statistical hypotheses. Springer Science & Business Media.
  • Moore, D. S., & McCabe, G. P. (2006). Introduction to the practice of statistics (5th ed.). W. H. Freeman.
  • Perneger, T. V. (2010). An introduction to statistical inference. French Edition.
  • Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern epidemiology. Lippincott Williams & Wilkins.
  • Sullivan, L. M. (2018). Essentials of biostatistics. Jones & Bartlett Publishers.
  • VanderWeele, T. J. (2019). Principles of confounder selection. European Journal of Epidemiology, 34(3), 211-219.
  • Wasserstein, R. L., & Lazar, N. A. (2016). The ASA’s statement on p-values: Context, process, and purpose. The American Statistician, 70(2), 129-133.

Related Assignments