Question 1
Alpha levels are methods that are primary measures of quantifying how significant a piece of statistical data is to the subjects under analysis. In health care practices and research, the standard alpha level is at 5% or 0.05. It is the optimum thrash hold for which medical research on drugs and diagnosis are foundationally evaluated (Curtis, 2015). However, there are instances during which the most relevant results are obtained when the Alfa levels are lower than the standard 0.05 level. Low alpha levels are considered in cases whereby there is a higher probability of introduction of errors. Seeing as there are errors that may not possibly be eliminated even with the performance of thorough statistical analyses, their significance is reduced by using low alpha levels (Schroeder, 2016) . Low alpha levels make null hypothesis related errors negligible. In normal terms, this essentially means that when a condition is falsely tested positive, this is good news because it will trigger further test to establish the causes of the error or better still will trigger a research on how to tackle the condition. However, when there is a false negative, physicians will assume that there is no condition and there will be no subsequent action. Meanwhile, the patient will be suffering silently.
Question 2
If a null hypothesis of research is of importance to the research, more than the other alternative research hypothesis, then Alpha 0.1 is considered. In this case, the researcher will channel their focus to the null hypothesis and ignore the others. Therefore, to embrace the null hypothesis, an alpha level slightly above the standard 0.05 levels is employed is considered within the research (Streiner, 2015) . This way the null hypothesis that is the point of focus is seen as viable and can be used to mainly because it is observable. In a study that involves large sample size, results that have a high alfa levels are frequently significant results. The hypothesis is further redefined to establish more efficient results.
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References
Curtis, M. J. (2015). Experimental design and analysis and their reporting: new guidance for publication in BJP. British journal of pharmacology, 172(14) , 3461-3471.
Schroeder, L. D. (2016). Understanding regression analysis. An introductory guide. Vol. 57 , 5- 8.
Streiner, D. L. (2015). Health measurement scales: a practical guide to their development and use. NY: Oxford University Press.
