Which of the following is a reason for deciding that a non-parametric test is more appropriate?Question 2Answera.The data is at an ordinal level of representation rather than interval or ratio.b.There is homogeneity of variance for the data for each of the experimental conditions.c.The data is normally distributedd.It will increase the chances of rejecting the null hypothesis due to the increased power of the test.

The most appropriate reason for deciding that a non-parametric test is more appropriate would be "a. The data is at an ordinal level of representation rather than interval or ratio."

Here's why:

a. Non-parametric tests are often used when the data is ordinal. Ordinal data is a type of categorical data with an order (i.e., there is a clear distinction between the categories, and they can be sorted or ranked). Non-parametric tests do not require the data to be on an interval or ratio scale, making them more flexible in terms of the level of measurement that the data must adhere to.

b. Homogeneity of variance is an assumption for parametric tests, not non-parametric tests. Therefore, this would not be a reason to choose a non-parametric test.

c. Normal distribution of data is another assumption for many parametric tests. Non-parametric tests do not require the data to be normally distributed, so if the data is normally distributed, it would not be a reason to choose a non-parametric test.

d. The power of a test is its ability to detect an effect if there is one. Non-parametric tests are generally considered to have less power than parametric tests, so if you wanted to increase the chances of rejecting the null hypothesis due to increased power, you would likely choose a parametric test, not a non-parametric one.