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Hypothesis testing is how one would be able to define a certain population. According to the text, Hypothesis testing involves drawing inferences about two different theory related to the population (Evans, 2013). Most healthcare uses hypothesis testing in medical packaging validation, to ensure that products reach consumers safely. Also, hypothesis testing is used to show that package creation will continue to produce the outcome they designed it for (Fotis,2008). The first step in hypothesis is to propose an educated guess, by utilizing the IF and AND within the statement. The null hypothesis represents an existing theory that is accepted, while the alternative hypothesis must be true if reject the null hypothesis (Evans,2013). Regarding the medical packaging validation, one would ask, are the sampling data misleading or do they support the decision to validate the production process?

The next step in the process is data would then be collected and summarized. This is where you want to gather enough data to draw conclusions about whether there is enough evidence to reject the null hypothesis. Next, evidence is collected to assess the testing to help draw the conclusions. This is when one would show a statistical inference through charts, by finding the p-value to define the level of significance. The significance level usually provides a good guideline for drawing conclusions.

The difference between parametric and nonparametric hypothesis testing is parametric takes the form of a normal distribution, this typically rely on quantitative data being nearly distributed to display the population parameters. Nonparametric is used on nominal, ordinal, and quantitative data (Lecky, 2019). If any differences exist, this would be found using the parametric data. In other words, non-parametric can be used on any data versus parametric can only be used on normal distribution.