Class, just to clarify substantive posts. The minimum word count should be no less than about 75 words so about 4 sentences at least. Substantive posts must be more than an agreement of another classmate’s or professor’s post, but you can add information that you’ve learned through your studies with scholarly sources.
Post 1:
The hypothesis is a statement about a prediction of what will happen between a parameter and a specific value. “In most instances, both the null and alternative hypotheses are written to direct the study. The null hypothesis indicates the lack of relationship between the variables or that there is no effect on the variables. The data will show the null hypothesis to be either true or false” (Ambrose, 2018). The null hypothesis is a statement that there is no difference between a parameter and a specific value. It is the starting point of an investigation. The alternative hypothesis is a statement that there exists a difference between a parameter and specific value. It is contrary to the null hypothesis.
Example #1
Hypothesis testing in research is used both in determining the effectiveness of medications. For example: Taking Claritin while receiving bone marrow stimulant injections.
Null Hypothesis: Taking Claritin when receiving a bone marrow stimulant has no effect on reducing bone pain.
Alternative Hypothesis: Taking Claritin when receiving a bone marrow stimulant will reduce the side effect of bone pain.
Criteria for rejecting the null hypothesis:
Data obtained in research shows patterns that either reject or fail to reject the hypothesis. If the data reveals a difference between the null hypothesis and the alternative hypothesis, then the null hypothesis can be rejected. As a result, the alternative hypothesis can be accepted. A failure to reject the null hypothesis indicates no effect is shown by the evidence” (Ambrose, 2018). “When a difference in an outcome (eg, pain) between exposures (eg, treatment groups) is observed, one needs to consider whether the effect is truly due to the exposure or if alternate explanations are possible” (Skelly, 2011). For example, the alternative explanations related to research on the use of Claritin when receiving bone marrow stimulants such as Filgrastim could be impacted by other factors. Maybe some patients were taking pain medications that improved their pain relief and may not have been the effect of Claritin. Alternative explanations need to be carefully considered.
Why important in practice and with patient interactions?
During my everyday interactions with patients, I receive questions about Claritin and its effect on reducing the side effect of bone pain. Some studies show no effect, and others show that it helps reduce bone pain. During patient interactions, it is important to know how to translate the research so that our patients can make educated decisions. My patients who tried Claritin have experienced no effect, while others have expressed that it helps relieve their bone pain.
Example #2
Research uses hypothesis testing to determine correlations or a relationship between things. In healthcare, we are always trying to improve the quality of life for our patients. We look to the clinical significance of the research. In healthcare, we can analyze how it applies to our patients and the impact on their health. It drives Evidence-based practice and is used to improve a process that more positively impacts patient care. At the hospital where I work, we continually try to improve the patient experience, safety, and processes related to patient care. In the continuously evolving age of healthcare, research and testing of different hypotheses are necessary to develop evidence-based interventions to provide quality patient care.
Reference
Ambrose, J. (2018). Clinical Inquiry and Hypothesis Testing. Applied Statistics for Health Care. Retrieved from https://lc.gcumedia.com/hlt362v/applied-statistics-for-health-care/v1.1/#/chapter/3
Skelly A. C. (2011). Probability, proof, and clinical significance. Evidence-based spine-care journal, 2(4), 9–11. https://doi.org/10.1055/s-0031-1274751
Post 2:
The hypothesis test is a statistical procedure that’s designed to use data to validate or invalidate a claim or test a claim or a question. The claim is usually made about a population or a parameter (one number that characterizes the whole population). Every hypothesis test contains a set of two opposing statements or hypotheses, about a population. The first is the null hypothesis, which states that the population parameter is equal to the claimed value.
If the null hypothesis is rejected, we must have an alternative hypothesis which is one you would believe if the null hypothesis were concluded untrue. More simply stated, the null hypothesis is the position you take before you examine the evidence, what you believe to be true. The alternative. The hypothesis is the position you are willing to move to if the evidence is strong enough. An example would be, I think this therapy will work better than the placebo. You then use data to support this. You test whether this is true by collecting a random sample of the effectiveness of the therapy and the placebo. If I do not find enough evidence to support that this therapy is better than the placebo, then I fail to reject the null hypothesis and the conclusion would be that the therapy does work better than the placebo. If the evidence was strong enough to reject the null hypothesis, the alternative hypothesis would be that the placebo worked better. Another example would be the null hypothesis is X medication ad Y medication have the same benefits. If we fail to reject the null hypothesis the alternative hypothesis would be that X medication is better or worse than Y medication. Quantitative research is important if we strive to practice by evidence-based practice the statistical process mentioned above must take place to realize when something needs to be changed or adjusted and have the evidence to back it up.
References
Popper, K. (2015, November). Discuss why this is important in your practice and with patient interactions. Retrieved from Quora: https://www.quora.com/In-hypothesis-testing-why-do-we-try-to-falsify-the-null-hypothesis-instead-of-research-hypothesis-alternate-hypothesis
Rumsey, D. J. (2011). Statistics for Dummies. Indianapolis: Wiley Publishing
Post 3:
PVALUE VERSUS ALPHA LEVEL
Class, what is the difference between a p value and alpha level? Please respond for a substantive credit.
