Non-Parametric Tests

What is the wilcoxon rank sum test used for?

• To compare the means of two dependent groups

• To analyze the variance between multiple groups

• To assess the correlation between two continuous variables

• To compare the medians of two independent groups

When should you use the Wilcoxon signed-rank test?

• When the data is normally distributed

• When you have independent data

• When the sample size is small

• When you have paired data and the data does not meet the assumptions of a parametric test.

What type of data is suitable for the Sign test?

• Interval data

• Ordinal data

• Ratio data

• Nominal data

What is the alternative hypothesis in the Wilcoxon Rank Sum Test (Mann-Whitney U test)?

• There is a difference between the distributions of the two groups being compared.

• There is no difference between the distributions of the two groups being compared.

• The test results are inconclusive.

• The test cannot be conducted due to lack of data.

What is the main difference between the Wilcoxon signed-rank test and the Sign test?

• Wilcoxon signed-rank test considers magnitude of differences, Sign test only considers direction

• Wilcoxon signed-rank test considers direction of differences, Sign test only considers magnitude

• Wilcoxon signed-rank test is used for independent samples, Sign test is used for paired samples

• Wilcoxon signed-rank test assumes normal distribution, Sign test does not assume normal distribution

What is the key assumption of the Sign test?

• The differences between pairs of observations are independent of each other.

• The data must be normally distributed

• The sample size must be large

• The observations must be continuous variables

Which of the following is not an advantage of using non-parametric tests?

• They are more powerful than parametric tests

• They do not require assumptions about the distribution of the data

• They are easier to interpret

• They are faster to compute

What is a disadvantage of non-parametric tests compared to parametric tests?

• They are less sensitive to small differences

• They are more robust to outliers

• They require larger sample sizes

• They are more accurate in estimating population parameters

When should non-parametric tests be preferred over parametric tests?

• When the data is normally distributed

• When the sample size is small

• When the assumptions of parametric tests are violated

• When the data is continuous