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It is safe to say that if a persons life depended on a good decision. The person making the decision would do the necessary collection of data to get it right! Making decisions in business is critical and the use of hypothesis testing can aid deciding. The word hypothesis has to do with making a claim about something that for the most part, on the face of it looks logical (Bateh, 2015). Hypothesis testing involves drawing inferences about two contrasting propositions relating to the value of a population parameter, such as a mean, proportion, standard deviation, or variance (Evans, 2013). A person could make a claim or assume that in a 10-mile radius that there are only German shepherd dogs in the neighborhood. The person who is observing this is basing what is seen during walking only in a certain area. But this is not telling the whole story about the areas 10-mile radius. In doing hypothesis testing, one must prove or disprove the hypothesis and do several experiments to truly find out the truth or falseness of what the person assumes about German shepherds in the area (Bateh, 2015). This is a real-world scenario and Im pretty sure that someone in this course has at least seen one German shepherd in the neighborhood. The fact of the matter here is that if there were sightings of the dog in the neighborhood, a person uses logic to determine the dogs presence. The logic is known as common sense. First, a pattern is noticed, Second, it is noted that the pattern might has happen by chance. Third, a reason to believe that it is not by coincidence, and lastly, it is concluded that the pattern did not happen by chance (Miller, 2017). Parametric statistics require that there are fundamental assumptions met to be able to use a given statistic with a data set. The process of a parametric statistic happens when sample characteristics are used to estimate population parameters (Martin, 2012). Such characteristics are from the known normal distribution. Now, nonparametric statistics does not require the fundamental assumptions that are used in parametric statistics. The fundamental assumptions are the differences between the two methods (Martin, 2012).