Ward F. Wright II
Trident University International
Introduction to Health Statistics (2021JUL05FT-1) BHD220
Module 3 SLP
Dr. Gozalians, S.
Part 2: Session Long Project
Develop Hypothesis
Before conducting any statistical analysis on the data collected., the hypothesis is developed where the null hypothesis may be nullified by the results and the alternative hypothesis accepted based on the finding. The hypothesis is formulated based on the aim or objective of the study and the research question to identify the problem investigated. The formulation of the hypothesis will involve the number of participants tested and those not tested in the population selected. The hypothesis is formulated as below:
Ho: There was a statistical difference between the number of participants tested in all age groups and the number of participants tested.
H1: There was a significant statistical difference between the number of participants tested and those tested in all age groups of the population
Discuss the concept of the null hypothesis in your response.
A hypothesis includes a null and an alternative hypothesis. Both hypotheses are opposite each other, represented by H0 for null and H1 for the alternative hypothesis. The H1 is the alternative the research approves from the research finding while H0 is disapproved or rejected. Both hypotheses represent different incidences where the null hypothesis represents a standard view while the alternative hypothesis represents a particular cause of an event. In simple terms, the null hypothesis indicates that things or events outcomes are the same, which the alternative hypothesis suggests otherwise.
Based on the hypothesis formulated where Ho: There was a statistical difference between the number of participants tested in all age groups and the number of participants tried, and H1: There was a significant statistical difference between the number of participants tested and those tested in all age groups of the population research results can be used to evaluate whether to accept or reject the hypothesis (Dopazo, 2020). If a researcher agrees with the alternative hypothesis, then they will automatically reject the null hypothesis.
P-value is used to determine the intensity of the hypothesis and evaluate any statistical significance in the variable under investigation. Where there is a statistical significance evident, then the study is reliable. Hypothesis testing involves using statistical evidence to determine whether to accept or reject the hypothesis (Scheel et al., 2020). After formulating the above hypothesis, a statistical analysis such as probability is conducted to evaluate any significant difference between the number of people tested for HIV and those not tested. The statistical evidence indicates a 0.63 probability that a person from the sample is not tested hence proof of a statistical difference between the two groups.
In other cases, various statistical evaluations, such as the Z-score, are used to identify the significance level of the data collected (Dopazo, 2020). The hypothesis is formulated on the score where a score above the given rage is accepted and a score below the popularity is rejected (Scheel et al., 2020). Based on the z-score, obtain validity of the research hypothesis is obtained, which helps in accepting or rejecting the research hypothesis.
In conclusion, research evaluation requires developing hypotheses that are tested against the test results of the data obtained in the research. Formulated hypotheses include the null hypothesis, which delays a relationship between the variable, and an alternative hypothesis that approves the existence of a connection between the variables (Scheel et al., 2020). Statistical testing is conducted to evaluate the validity and reliability of the data, which leads to either accepting or rejecting the hypothesis. Acceptance of the alternative hypothesis leads to automatic rejection of the null hypothesis. Based on the accepted hypothesis, conclusions are drawn for the research.
References
Brémaud, P. (2020). Probability Theory and Stochastic Processes. Springer Nature.
Dopazo, J. (2009). Formulating and testing hypotheses in functional genomics. Artificial intelligence in medicine, 45(2-3), 97-107. https://doi.org/10.1016/j.artmed.2008.08.003
Norman, G. R., & Streiner, D. L. (2014). Section the first: The nature of data and statistics: Chapter 6: Elements of statistical inference. In Biostatistics: The bare essentials [4th ed., e-Book]. Shelton, Connecticut: PMPH-USA, Ltd. eISBN-13: 978-1-60795-279-4. Available in the Trident Online Library EBSCO eBook Collection.
Scheel, A. M., Tiokhin, L., Isager, P. M., & Lakens, D. (2020). Why hypothesis testers should spend less time testing hypotheses. Perspectives on Psychological Science, 1745691620966795. https://doi.org/10.11771745691620966795
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