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Hypothesis testing is when you draw a conclusion from two contrasting propositions relating to the value of a population parameter, such as a mean, proportion, standard deviation, or variance (Evans, 2013 p. 139). Hypothesis testing consists of null hypothesis and alternative hypothesis propositions. The null hypothesis is what is already known where the alternative hypothesis is what new information has been discovered through sampling. Based on the new information provided you determine if the null hypothesis needs to be rejected and support the alternative hypothesis or whether you determine that the information doesnt support the alternative hypothesis. Most hypothesis tests are initiated with confidence to reduce the margin of error. In order to conduct a hypothesis test you have to figure out what hypotheses you want to test and then determine what risk would come from drawing an incorrect conclusion about the hypothesis that is true. The next step would be to determine the decision rule for your conclusion and then you collect the data to calculate a test static. The last step is to apply the information gathered by the previous step resulting in your conclusion (Evans, 2013, p.140). Hypothesis testing can have four outcomes: the null hypothesis is actually true and the test doesnt reject it, the null is false and the results prove it, the null is true but the test incorrectly rejects it, and the null is false but the test fails to reject it (Evans, 2013, p. 141-142). The main difference between parametric and nonparametric hypothesis testing is your ability to meet the sample size requirements. Parametric will give you a better conclusion as well as valid results. The larger your sample size youll be able to calculate a larger number of variations in the responses. The nonparametric testing will only contain the responses of a smaller group of people allowing for grouping of data (minitab.com).