True or false? The main difference between a parametric and a non-parametric test is the assumption that the data follow a specific distribution.A:    FalseB:    True

Non-parametric tests are hypothesis testing procedures that assume the variables of interest are measured on nominal or ordinal scales.Select one:TrueFalse

From the below list, when is it ideal to apply parametric test?When the independent and dependent variables are nominal and the data is not normally distributed.When the independent and dependent variables are ordinal and the data is not normally distributed.When the independent and dependent variables are interval and the data is normally distributed.When the independent and dependent variables are ordinal and the data is normally distributed.

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.

Select Any One Of the Following Options: Which of the following tests is conducted before deciding to apply parametric or non-parametric tests?Normal distribution test (Frequency test)Median testMean testChi-square test

Which of the following is not a non-parametric factor in deciding on the specific test to apply?A:    Type of dataB:    Sample sizeC:    Number of groups