Introduction to Inferential Statistics and SPSS-TOPIC 3 DQ 1

Introduction to Inferential Statistics and SPSS-TOPIC 3 DQ 1


QUESTION- Summarize the six steps of hypothesis testing. Propose a scenario in which hypothesis testing is applied to public health data.

Six Steps of Testing Hypothesis
Hypothesis testing is a statistical method used to analyze and test assumptions concerning a specific population parameter. Hypothesis testing allows the analyst to compare the result with the null hypothesis. It is only rejected when the probability is below the predetermined level of significance. There are six essential steps that analysts use to test the hypothesis. The first step is setting the hypothesis by defining the research, null and alternate hypotheses (Yin, 2017). The research hypothesis is the assumption that is considered true for the investigation. The research hypothesis outlines a statement that will guide the entire research. The alternate hypothesis is the position that states something happens but offers a new theory instead of the null hypothesis. Thus, the researcher is required to differentiate between the general research hypotheses and the null hypothesis.
The second step is developing assumptions that will guide the hypothesis testing. Different factors are considered when developing hypothesis testing assumptions. These include’ the data measurement level, the testing significance level, characteristics of the sample when applying the statistics testing, sampling method and sample size, data distribution and population characteristics knowledge. This information provides ideal background that allows the researcher to set the right parameter for analyzing and testing the hypothesis.
The third step is defining the test statistics that will be used during the process. The research must elaborate the confidence interval structure by highlighting the notations and equations used to test levels of significance. The fourth step involves highlighting the probability statement and outlining the region of rejection. Before starting calculations, the researcher must highlight the test used to reject or fail to reject the null hypothesis(Flores-Ruiz et al., 2017). The definition of rejection areas allows them to determine critical values when deciding.
The fifth step is calculating the based on the sample provided using the highlighted statistical methods. The researcher must ensure all the calculation parameters have been considered and adhered to during the discussion. The testing of the statistic measure requires the integration of all notation and equations. The last process highlights the conclusion by indicating the acceptance or rejection of the null hypothesis and the statement of result. The last step also allows the researchers to highlight or recommend future research progress (Simonsohn et al., 2019). Hypothesis testing is used in public health to identify whether new drugs can manage certain disorders. For instance, hypothesis testing has been conducted to determine the viability of different COVID 19 in preventing corona virus.

Flores-Ruiz, E., Miranda-Novales, M. G., & Villasís-Keever, M. Á. (2017). The research protocol VI: How to choose the appropriate statistical test. Inferential.
Simonsohn, U., Simmons, J. P., & Nelson, L. D. (2019). Specification curve: Descriptive and inferential statistics on all reasonable specifications. Available at SSRN 2694998.
Yin, R.K., (2017). Case study research and applications: Design and methods. Sage publications

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Catherine Moma Reply

I agree with you that hypothesis testing is central to public health. Through hypothesis testing, an adequate evaluation of the strength of evidence from a sample can be determined, while hypothesis testing can provide a framework for making determinations that might be related to a larger population. Hypothesis testing, therefore, provides a method that can help one to understand how reliably one can be involved in extrapolating the different observed findings that might be found in a given study and the extent to which specific conclusions can be applied to a larger population (Sacha & Panagiotakos, 2016). In a research study, an investigator will therefore formulate a particular hypothesis and be actively involved in evaluating data from the given sample. After assessing data from a given sample, a researcher can therefore support or refute the particular hypothesis (Sacha & Panagiotakos, 2016).

A reasonable hypothesis should therefore be stated in advance, be specific, and straightforward. Hypothesis will therefore be critical in research, especially on issues related to public health, because it helps to ensure that all research methodologies are varied and scientific. On the other hand, the hypothesis will be vital because it helps assume the probability of progress or failure of a given research study (Sacha & Panagiotakos, 2016). Hypothesis will also be vital in a research study as it will provide a link between specific research operations and underlying theories. On the other hand, a hypothesis will be critical in helping to measure the reliability and validity of the research and data analysis. Hypothesis will also provide a basis to prove the validity and evidence of the research and to effectively describe a research study in concrete terms as opposed to theoretical terms (Sacha & Panagiotakos, 2016).





Sacha, V., & Panagiotakos, D. B. (2016). Insights in Hypothesis Testing and Making Decisions in Biomedical Research. The Open Cardiovascular Medicine Journal, 10(1), 196–200.