Application and interpretation of Public Health data-TOPIC 3 DQ 1
Topic 3: Introduction to Inferential Statistics and SPSS
QUESTION-TOPIC 3 DQ 1
Summarize the six steps of hypothesis testing. Propose a scenario in which hypothesis testing is applied to public health data.
Hypothesis testing is a scientific method used by researchers to investigate ideas and theories in a study. The testing helps in the development of approaches and methodologies considering both the independent and dependent variables. Hypothesis testing provides a baseline foundation for scientific researchers, engineers, and those in other technical professions to reach a desirable agreement regarding their diverse hypotheses in their work areas. The following study focuses on the steps of hypothesis testing and the application of hypothesis testing in public health data.
Summary of Steps of Hypothesis Testing
Hypothesis testing has six steps in verifying whether to accept or reject the hypothesis. The steps are as follows; determining the null hypothesis mainly involves observing the idea of no change (Phang, 2018). Finding the alternative hypothesis that can be unilateral or bilateral, depending on the setting of the problem under investigation, follows (Phang, 2018). Testing the test statistic entails calculating the alternative hypothesis from the sampled data (Ioannidou & Erduran, 2021).
The last three steps involve critical analysis to derive the final hypothesis for the study. In analyzing data, it is crucial to determine the significance level or critical P-value. All hypothesis testing is subject to errors, either Type I or II error. Based on the types of errors, it can be easier to determine whether to accept or reject the hypothesis. The final step is concluding based on the size of the P-value. For instance, if the test value is smaller than the critical P-value, the null hypothesis is rejected. If greater, the null hypothesis is accepted, and the significance level is stated (Phang, 2018).
Hypothesis Testing in Public Health Data
A microbiome study conducted hypothesis testing and statistical analysis to explore the systematic concepts and their relationship among hosts, microorganisms, and surroundings, if at all there exists any (Xia& Sun,2017). The hypothesis testing shows the need for more tests in developing methods and models suitable for analyzing microbiome structural data due to its complex data taking too long to be analyzed and understood (Xia& Sun,2017). It is vital to consider this to help statisticians manage their work and conclude several ideas and views.
Ioannidou, O., & Erduran, S. (2021). Beyond hypothesis testing. Science & Education, 30(2), 345-364. https://link.springer.com/article/10.1007/s11191-020-00185-9
Phang, R. L. (2018). Basic concepts in hypothesis testing. https://sms.math.nus.edu.sg/smsmedley/Vol-16-2/Basic%20concepts%20in%20hypothesis%20testing(Rosalind%20L%20P%20Phang).pdf
Xia, Y., & Sun, J. (2017). Hypothesis testing and statistical analysis of microbiome. Genes & diseases, 4(3), 138-148. https://www.sciencedirect.com/science/article/pii/S2352304217300351