Provide two different examples of how research uses hypothe

Topic 3 DQ 2

The aspect of hypothesis testing refers to the process of creating inferences or otherwise referred to as educated guesses concerning a specific research parameter. Hypothesis testing can be conducted either through the use of uncontrolled observational study or statistics and sample data (Mellenbergh, 2019). Prior to testing a hypothesis, it is essential to come up with the degree of statistical significance in the hypothesis since a researcher cannot be 100 percent on the educated guess.

An example of the use of hypothesis testing is in determining the prevalence of common cold in children who take vitamin C. The null hypothesis would state that the prevalence of flu in children who take vitamin C is similar to those who don’t take vitamin C. the alternative hypothesis would be that children with the uptake of vitamin C have a reduced prevalence of flu in flu seasons.

Another example would be research to identify if therapy is more effective than a placebo. In order to reject the null hypothesis, a redetermined number of subjects among the hypothesis test have to prove the alternative hypothesis. The proof will then overturn the original null hypothesis, which will then be rejected.

Hypothesis testing is an important aspect of statistics and research as it provides a basis for understanding whether something actually occurred or if certain groups or sets of data are different from each other (Dubois, 2017). Hypothesis testing also helps in identifying if an aspect of the research has more positive effects or if a variable can predict another to form a basis for defining a conclusion. With the help of the calculated probability (p-value), one can easily determine the inclination of the research based on either the null hypothesis of the alternative hypothesis.

References

Dubois, S. (2017). The Importance of Hypothesis Testing. (2020). Retrieved 18 May 2020, from https://sciencing.com/the-importance-of-hypothesis-testing-12750921.html

Mellenbergh, G. J. (2019). Null Hypothesis Testing. In Counteracting Methodological Errors in Behavioral Research (pp. 179-218). Springer, Cham. https://link.springer.com/book/10.1007/978-3-030-12272-0

Topic 3 DQ 2: Hypothesis Testing and Confidence Intervals

Hypothesis tests and confidence intervals are related in the sense that they both are inferential methods that are based on an approximated sampling distribution. The hypothesis tests make use of data from a given sample to test the predetermined hypothesis (Sacha & Panagiotakos, 2016). On the other hand, confidence intervals make use of data from the sample to provide an estimate of the population parameter. In this manner, it is evident that the simulation methods that are used in the construction of the bootstrap distribution, as well as the randomization distributions, are identical.

Confidence intervals are made up of a range of reasonable estimations concerning population parameters. For instance, a two-tailed confidence interval is applied in a two-tailed hypothesis testing. In health care research, a confidence level of 95 percent is mostly used. The level indicates the significance of health research with regards to being precise and accurate with health care data (Hazra, 2017).

For instance, while conducting research on the effect of therapy or medication on patients with mental health conditions. The calculation of the p-value will allow the researcher to achieve the results of the null hypothesis. With a low p-value, a researcher is able to comprehend that there is stronger support for the alternative hypothesis.

In the workplace setting, research can be conducted on the impacts of evidence-based practice on patient outcomes. Hypothesis testing will facilitate the identification of educated guesses, while the confidence interval will provide a basis for the statistical confidence level that will be used in the research (Sacha & Panagiotakos, 2016). The research will then be used to provide a recommendation for the viability of the EBP.

References

Hazra A. (2017). Using the confidence interval confidently. Journal of thoracic disease, 9(10), 4125–4130. https://doi.org/10.21037/jtd.2017.09.14

Sacha, V., & Panagiotakos, D. B. (2016). Insights in Hypothesis Testing and Making Decisions in Biomedical Research. The open cardiovascular medicine journal, 10, 196–200. https://doi.org/10.2174/1874192401610010196

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