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When choosing a statistical test to perform it is important to understand the type of data you are working with. Each type of data; nominal-, ordinal-, interval-, and ratio- type number since they all tell a different story. We also must assess the skewness of the data as this will help us choose how to present the data. We also need to assess whether the explanatory variable can be manipulated or controlled. This will help discern if it can be a correlational, experimental, or quasi-experimental statistical test (Corty, 2016). Another parameter that we should check before conducting a statistical test is checking whether there is a confounding variable that exists. This will eliminate experimental tests. If we want to know if there is a relationship between the two variables, we would look to correlational tests (Corty, 2016). If we wanted to know if different populations possess different amounts of the variable, we will look to either experimental or quasi-experimental tests (Corty, 2016).
These parameters are important for public health testing because the goal is to promote wellness and increase the livelihood of the population. In order to do this, we need measurable data points that will accurately and precisely describe variables. The better we understand where the numbers and data are coming from, the better we can choose the right course of action which will lead to the best possible outcome (preferably).
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