# Set up a nonparametric test using the six step hypothesis testing procedure you do not have to provi

In the upper portion of the figure, certainly 10 is worse than 9, which is worse than 8; however, the difference between adjacent scores may not necessarily be the same.

### Set up a nonparametric test using the six step hypothesis testing procedure you do not have to provi

However, there are situations in which assumptions for a parametric test are violated and a nonparametric test is more appropriate. Linearity — You have two independent variables and so you should create two scatter charts: 1 independent variable 1 with the dependent variable and 2 independent variable 2 with the dependent variable. In fact, for large samples it tends to be less critical to check for normality since the Central Limit Theorem will kick in. Practical Significance vs. It can sometimes be difficult to assess whether a continuous outcome follows a normal distribution and, thus, whether a parametric or nonparametric test is appropriate. The power curve for the process under discussion is shown below. The table is divided into two sections: parametric and non-parametric. The Breusch-Godfrey test can be useful in this case. These include situations: when the outcome is an ordinal variable or a rank, when there are definite outliers or when the outcome has clear limits of detection. When the sample size is small and the distribution of the outcome is not known and cannot be assumed to be approximately normally distributed, then alternative tests called nonparametric tests are appropriate. Sometimes pain scales use visual anchors as shown in the lower portion of the figure below. The cost of fewer assumptions is that nonparametric tests are generally less powerful than their parametric counterparts i. Ordinal outcomes can be less specific as the ordered categories need not be equally spaced. There are some situations when it is clear that the outcome does not follow a normal distribution.

You can remove multicollinearity by removing one possibly more of the variables that is causing multicollinearity. Non-parametric tests are used when there is no assumption of a specific underlying distribution of the data.

The most practical approach to assessing normality involves investigating the distributional form of the outcome in the sample using a histogram and to augment that with data from other studies, if available, that may indicate the likely distribution of the outcome in the population. Visual Pain Scale In the upper portion of the figure, certainly 10 is worse than 9, which is worse than 8; however, the difference between adjacent scores may not necessarily be the same. HIV viral load is a measure of the amount of virus in the body and is measured as the amount of virus per a certain volume of blood. Interval data are like continuous data in that they are measured on a constant scale i. For example, when running tests of hypothesis for means of continuous outcomes, all parametric tests assume that the outcome is approximately normally distributed in the population. Rated 6/10 based on 88 review