The fourth step involves running a test get results

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which to reject or accept the null hypothesis. The fourth step involves running a test-get results and making decisions based on step three above. In this step, the decision on whether or not the test results are statistically significant based on the p-value is made. The last step is analyzing the results and giving insight into the problem by stating the conclusion in the setting of the original problem. In hypothesis testing, type I errors ensue when the accurate null hypothesis is rejected. This category of error can be controlled, and the value of alpha that is related to the selected level of significance has a direct pertinence on these errors (OutLieer, 2012). Conversely, type II errors arise when the wrong null hypothesis is not rejected. It arises when the null hypothesis is stated to be true when it is false. A decision outcome can be improved by reducing the type I or type II errors. Although the errors cannot be eliminated, one kind of error can be minimized when its probability is
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HYPOTHESIS TESTING 3 decreased while increasing the likelihood of the other type (Gaston, 2014). The value of alpha can be reduced from 0.05 to 0.01, and this will ensure improvements in the decision outcome.
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HYPOTHESIS TESTING 4 References Gaston, L. (2014). Hypothesis testing made simple . California: CreateSpace Independent Publishing Platform. MrClean1796. (2015). Hypothesis testing in research & law (Concept). YouTube . Retrieved from OutLieer. (2012). Stats 1- video -13: 5 steps to hypothesis testing. YouTube . Retrieved from
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  • Spring '16
  • Statistics, Hypothesis testing, Statistical hypothesis testing, Statistical significance, Type I and type II errors

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