A hypothesis test is a statistical method used to evaluate a hypothesis about a population. It allows individuals to choose between two different hypotheses pertaining to a population parameter. When conducting the test, it is imperative to identify the null hypothesis and the research hypothesis (Sullivan, 2011). Next, a random sample is obtained from the population and the sample data collected from the population compared with the prediction that was made from the hypothesis (Sullivan, 2011). If there is a big discrepancy, it means the hypothesis formulated is wrong. If the sample mean is consistent, the hypothesis is reasonable.
I can profoundly say that chapter 7 –hypothesis testing procedures –has given me basic skills in statistics. I now have a clear understanding of hypothesis testing. I know how to perform a hypothesis test, estimate and interpret p-values, perform analysis of variance by hand and interpret the results, perform chi-square test by hand and interpret the results, and identify the appropriate hypothesis procedures to use when given the outcome variable and size of the sample. The chapter has given me a clear understanding of the role of statistics in life.
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In hypothesis testing, one has to formulate the null hypothesis and the alternative hypothesis when trying to establish the outcomes of a certain study. A null hypothesis is a hypothesis the researcher attempts to disapprove while the alternative hypothesis is the hypothesis that states that there is a statistically significant relationship between the variables being studied (Veazie, 2015).
Test statistics sets the rules for the hypothesis formulated. The test statistic, such as the z statistic, summarizes the sample information. However, test statistics are used when the sample is small. The next step is set up a decision. In this step, the circumstances to reject the null hypothesis are outlined. The decision rule is based on specific values of the tests statistic. For instance, if the z statistic is 1.56, the decision rule is to reject H 0 if
Generally, for any given study, the decision rule for a specific test depends on the type of research or alternative hypothesis, the test statistic, and the level of significance (Sullivan, 2011).
Ideally, we always reject the null hypothesis when it is false. But two other scenarios are possible, each of which leads to an error. The first scenario involves the rejection of the null hypothesis, that is actually true. This type of error is referred to as type I error. The other kind of error consists in accepting a null hypothesis that is false. This kind of error is referred to as type II error. Although type I and type II errors cannot be eliminated entirely, one can be minimized. When the probability of one type of error is reduced, the probability of the other type of error increases (Sullivan, 2011). Thus, they type of error one wants to decrease depends on if the individual is more accepting of type I or type II. Based on this decision, the researcher or experimenter can proceed to design his or her statistical experiment.
The chapter has not changed my thoughts about hypothesis testing procedures. I was well versed with all the contents presented in the chapter. I had the knowledge of significance level, critical value, and p-value and how they are applied in hypothesis testing. The chapter helped me to tell the casual effect on one another.
The chapter has given me a clear understanding of how to carry out a hypothesis test. The chapter is very informative and easy to understand. It clearly outlines the procedures to follow in order to interpret chi-square results appropriately. Overall, I can profoundly say that the chapter has increased my understanding of the basic skills of statistic. In the health care industry, these skills are required, especially when conducting studies that involve descriptive statistics.
References
Sullivan, L. (2011). Essentials of biostatistics in public health (2 nd Edition). Jones & Bartlett Learning.
Veazie, P. (2015). Understanding statistical testing. SAGE Journals. [Online]. Available at: https://journals.sagepub.com/doi/full/10.1177/2158244014567685 . Accessed 29 th September 2019.
