Hypothesis testing is an essential activity that is part of physiological research and evidence-based medicine. A useful hypothesis should be able to come up with a solution that is considerably close to the answer required and also compares two data sets. The guarantee of a valid hypothesis relies on an effective research question. As a result, the hypothesis should adhere to a few standards such as simplicity, have specifications and should be defined in advance.
The four potential outcomes of hypothesis testing focus on the overview of the errors; the type 1 and type 2 errors, the power similarly referred to as the p-value, the smallest effect of interest, and the variability. A type I error may arise if a researcher ignores the null hypothesis which is valid on the population tested. However, type 2 errors often appear in case the analyst declines to ignore the null hypothesis appearing false in the population. The fact that type 1 and type 2 errors are entirely irresistible, the researcher should reduce their occurrence by adjusting the volume of the sample (Banerjee, et al., 2009). An increase in the volume sample, creates lesser chances, making it differ extensively from the population.
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One of the primary health issues that will be affecting the healthy people in 2020 is the public health challenge. Public health functions are to better the lives of people through the prevention and treatment of diseases such as mental illnesses. To best exemplify the concept of hypothesis, for instance in a case where an investigator wants to analyze whether dental services in his country is the same as that in another nation, he or she could come up with a hypothesis to help him or her validate the findings. The first step is to define the hypothesis and to determine the level of significance. The second approach involves selecting the appropriate test static. The statistical test aims to reject the null hypothesis which describes the state of no observable change or behavior. The next procedure is to set up a decision rule. Another step is the computation of test statistics (Shaffer & Popper, 1995). Finally, the last process involves the conclusion of the findings obtained after data analysis.
References
Banerjee, Amitav, Chitnis, U. B., Jadhav, S. L., Bhawalkar, J. S., & Chaudhury, S. (2009). Hypothesis testing, type I and type II errors. Industrial psychiatry journal, 18 (2), 127.
Shaffer, & Popper, J. (1995). Multiple hypothesis testing. Annual review of psychology, 46 (1), 561-584.
