Non-Parametric Statistics: A Comprehensive Guide for Psychology Students

Question 1

Which of the following is a key assumption of non-parametric statistical tests?

Accepted Answer

Data is at least ordinal in nature.

Answer

Variables are normally distributed.

Answer

Relationships between variables are linear.

Answer

Sample size is large.

Question 2

In psychological research, when are non-parametric tests more advantageous to use compared to parametric tests?

Accepted Answer

When the data does not meet the assumptions of parametric tests, such as non-normal distribution or unequal variances, or when the sample size is small.

Answer

When the data meets the assumptions of parametric tests, such as normal distribution and equal variances.

Answer

When the sample size is very large, making the assumptions of parametric tests less important.

Answer

When the researcher is interested in making inferences about the population mean, which non-parametric tests cannot provide.

Question 3

What is a crucial assumption of non-parametric tests?

Accepted Answer

Data points must be independent.

Answer

Data must follow a normal distribution.

Answer

Data must be continuous and numerical.

Answer

Sample size must be large.

Question 4

Which assumption is unique to parametric statistical tests and not required for non-parametric tests?

Accepted Answer

Data must be normally distributed.

Answer

Observations must be independent.

Answer

Data must be measured on a continuous scale.

Answer

Sample size must be small.

Question 5

In non-parametric tests, what's the key assumption about the data?

Accepted Answer

Observations are independent.

Answer

It follows a normal distribution.

Answer

Sample size meets a minimum.

Answer

Data is measured on an interval or ratio scale.

Question 6

What is the primary difference between parametric and non-parametric statistical tests?

Accepted Answer

Parametric tests assume a normal distribution of data, while non-parametric tests do not.

Answer

Non-parametric tests are more powerful than parametric tests.

Answer

Non-parametric tests are always appropriate for large sample sizes.

Answer

Parametric tests can only analyze continuous data.

Question 7

Which assumption is unique to parametric tests?

Accepted Answer

The data is normally distributed.

Answer

The data is sampled randomly from a larger population.

Answer

The data is measured on an interval or ratio scale.

Question 8

Which of the following is a key assumption of non-parametric statistical tests?

Accepted Answer

The population distribution is unknown.

Answer

The data is continuous.

Answer

The data is randomly sampled.

Answer

The sample size is large.

Question 9

Which of the following is NOT an assumption of non-parametric tests?

Accepted Answer

The data must be normally distributed.

Answer

The data must be independent.

Answer

The data must be measured on an ordinal or interval scale.

Answer

The sample size must be large.

Question 10

Which assumption is essential for parametric tests but not for non-parametric tests?

Accepted Answer

Data must be normally distributed.

Answer

Data must be independent.

Answer

Measurement scale must be at least ordinal.

Answer

Sample size must be large.

Question 11

What sets non-parametric tests apart from parametric tests?

Accepted Answer

They don't assume data follows a normal distribution.

Answer

They always need a large sample size.

Answer

They're only suitable for interval or ratio data.

Answer

They're more powerful than parametric tests.

Question 12

Which assumption is exclusive to parametric tests?

Accepted Answer

Data are normally distributed

Answer

Data are independent

Answer

Data are measured on at least an ordinal scale

Question 13

Which assumption is exclusive to parametric tests and not necessary for the Mann-Whitney U test?

Accepted Answer

(B) Continuous data distribution

Answer

(A) Random sampling

Answer

(C) Independent samples

Answer

(D) Equal sample sizes

Question 14

What is the defining characteristic that distinguishes non-parametric tests from parametric tests?

Accepted Answer

Non-parametric tests do not assume a specific distribution shape for the data.

Answer

Non-parametric tests only analyze data measured on nominal or ordinal scales.

Answer

Non-parametric tests are always more powerful than parametric tests.

Answer

Non-parametric tests are less sensitive to outliers than parametric tests.

Question 15

Which of the following best describes the purpose of non-parametric statistical tests?

Accepted Answer

To analyze data that does not meet the assumptions of parametric tests

Answer

To make assumptions about the distribution of the data

Answer

To analyze data from small sample sizes only

Answer

To provide more powerful results than parametric tests

Question 16

Which characteristic is essential for conducting parametric tests but not for non-parametric tests?

Accepted Answer

Normally distributed data

Answer

Interval or ratio measurement level

Answer

Large sample size (n > 30)

Question 17

What is a defining characteristic of non-parametric statistical tests?

Accepted Answer

They make fewer assumptions about the data than parametric tests.

Answer

They are always more powerful than parametric tests.

Answer

They are the preferred choice for all statistical analyses.

Answer

They require extremely large sample sizes.

Question 18

For a chi-square test to produce dependable results, which requirement must be fulfilled?

Accepted Answer

Expected frequencies should be at least 5 for each category.

Answer

Data should be in ordinal or continuous format.

Answer

Sample size should be over 100 participants.

Answer

Data should follow a normal distribution.

Question 19

For the chi-square test to be valid, which assumption must be met?

Accepted Answer

The variables are independent.

Answer

The data is normally distributed.

Answer

The sample size is large.

Question 20

Regarding the Kruskal-Wallis test, is a minimum sample size per independent group a requirement?

Accepted Answer

No, there is no specific minimum sample size requirement per group

Answer

Yes, a minimum of 5 observations per group is required

Answer

Yes, a minimum of 10 observations per group is required

Answer

Yes, a minimum of 15 observations per group is required

Question 21

In the context of chi-square tests, which assumption ensures that the expected frequencies in each cell of the contingency table are adequate for the test to be valid?

Accepted Answer

The expected frequency in each cell of the contingency table must be at least 5.

Answer

The sample size must be at least 30.

Answer

The chi-square statistic must be significant.

Answer

The data must be normally distributed.

Question 22

In psychological research, the Kruskal-Wallis test is primarily used to compare:

Accepted Answer

Medians of more than two independent groups

Answer

Means of two dependent groups

Answer

Proportions of two independent groups

Question 23

When conducting the Wilcoxon signed-rank test, which assumption must be met for the results to be valid?

Accepted Answer

The data must be measured on at least an ordinal scale.

Answer

The data must be normally distributed.

Answer

The data must be independent.

Answer

The sample size must be large enough to ensure normality.

Question 24

In Spearman's correlation, what does a negative correlation coefficient indicate?

Accepted Answer

A negative relationship between the ranked variables.

Answer

A positive relationship between the ranked variables.

Answer

No relationship between the ranked variables.

Answer

The strength of the relationship between the ranked variables cannot be determined from the correlation coefficient.

Question 25

In a chi-square test of independence, which assumption is crucial for the test's validity?

Accepted Answer

Observations in each cell of the contingency table are independent.

Answer

Data follows a normal distribution.

Answer

Sample size is sufficiently large.

Answer

Data is measured on a continuous scale.

Question 26

Which assumption is crucial for the appropriate use of the Wilcoxon signed-rank test?

Accepted Answer

Samples must be matched or paired.

Answer

Data must be normally distributed.

Answer

Samples must be independent.

Answer

Data must be measured on an interval or ratio scale.

Question 27

What is the fundamental difference between non-parametric and parametric statistical tests?

Accepted Answer

Non-parametric tests make no assumptions about the distribution of the data, while parametric tests assume a normal distribution.

Answer

Non-parametric tests require a normal distribution of data, while parametric tests do not.

Answer

Non-parametric tests require larger sample sizes than parametric tests.

Question 28

When performing a chi-square test, which assumption must be met?

Accepted Answer

Expected frequency in each cell is at least 5.

Answer

Data follows a normal distribution.

Answer

Data is measured at the ordinal level or higher.

Answer

Sample size is large.

Question 29

Which of the following is a characteristic of non-parametric tests?

Accepted Answer

They can be used when the data does not meet the assumptions of parametric tests.

Answer

They are more powerful than parametric tests.

Answer

They are only suitable for small sample sizes.

Answer

They are always more conservative than parametric tests.

Question 30

Which of the following is an assumption of the Kruskal-Wallis H test?

Accepted Answer

Observations must be independent.

Answer

Observations must be normally distributed.

Answer

Sample sizes of groups must be equal.

Answer

Dependent variable must be continuous.

Question 31

When conducting a Mann-Whitney U test, which assumption must be met for the results to be valid?

Accepted Answer

The two groups must be independent of each other.

Answer

The data must be normally distributed.

Answer

The data must be measured at the interval or ratio level.

Answer

The sample size must be large enough to ensure normality.

Question 32

What are the potential consequences of violating the assumptions of non-parametric statistical tests, and how can it impact the reliability of the results?

Accepted Answer

Incorrect conclusions and reduced statistical power

Answer

No impact on the validity of the results

Answer

Only affects the significance level of the results

Question 33

In a survey, participants rank their top three preferred music genres from a list of 10 options. What type of data is collected? Why would a non-parametric test be suitable for analyzing this data?

Accepted Answer

Ordinal data, as the responses can be ranked but do not represent equal intervals.

Answer

Categorical data, as the responses are unordered categories.

Answer

Interval data, as the responses represent equal intervals between ranks.

Answer

Ratio data, as the responses have a meaningful zero point.

Question 34

Which non-parametric statistical test is used to compare the distributions of two independent groups?

Accepted Answer

Chi-square test

Answer

t-test

Answer

F-test

Answer

ANOVA

Question 35

When is the chi-square test an appropriate choice?

Accepted Answer

When the data is categorical and the variables are independent

Answer

When the data is normally distributed and the sample size is large

Answer

When the data is ordinal and the groups are equal in size

Answer

Never

Question 36

What is the purpose of the Kruskal-Wallis test?

Accepted Answer

To compare the medians of three or more independent groups

Answer

To compare the variances of two independent groups

Answer

To determine if a categorical variable is related to an ordinal variable

Answer

To determine if a sample comes from a specific distribution

Question 37

What is Type I error in the context of non-parametric statistical tests?

Accepted Answer

Rejecting the null hypothesis when it is true

Answer

Accepting the null hypothesis when it is false

Answer

Making a mistake in calculating the test statistic

Answer

Using the wrong non-parametric test

Question 38

In which scenario would it be appropriate to use a non-parametric statistical test?

Accepted Answer

Comparing the satisfaction ratings of two different customer service groups

Answer

Determining if the mean height of male and female students is different

Answer

Testing the hypothesis that a new drug is effective in reducing blood pressure

Answer

Predicting the likelihood of a patient recovering from an illness

Question 39

Distinguish the chi-square goodness-of-fit test from the chi-square test of independence.

Accepted Answer

The goodness-of-fit test compares observed frequencies to expected frequencies under a specific distribution, while the test of independence compares observed frequencies to expected frequencies under the assumption of independence.

Answer

The test of independence is more powerful than the goodness-of-fit test.

Answer

The goodness-of-fit test is more difficult to calculate than the test of independence.

Answer

There is no difference between the two tests.

Question 40

In non-parametric statistical tests, why is it crucial to grasp the assumptions and the repercussions of violating them?

Accepted Answer

To ensure the validity of the test results

Answer

To adhere to research standards

Answer

To boost the test's statistical power

Answer

To simplify data analysis

Question 41

When conducting a Mann-Whitney U test, which assumption must be met?

Accepted Answer

Data must be independent.

Answer

Data must be normally distributed.

Answer

Variances of the two groups must be equal.

Answer

Data must be continuous.

Question 42

Which of the following is a distinct feature of non-parametric tests?

Accepted Answer

They do not assume a normal distribution.

Answer

They necessitate normally distributed data.

Answer

They are less powerful than their parametric counterparts.

Answer

They can only be used with very large sample sizes.

Question 43

When comparing two ordinal samples on a single characteristic, which non-parametric test is most appropriate?

Accepted Answer

Mann-Whitney U test

Answer

Chi-square test

Answer

Kruskal-Wallis test

Answer

One-sample t-test

Question 44

In the context of the chi-square test, what is the stated null hypothesis?

Accepted Answer

The two categorical variables are independent.

Answer

The two categorical variables are dependent.

Answer

The two samples stem from distinct populations.

Answer

The two samples possess identical means.

Question 45

What primary purpose do post-hoc tests serve?

Accepted Answer

To pinpoint specific group differences after a significant ANOVA outcome

Answer

To gauge the degree of a distribution's skewness

Answer

To establish the statistical power of a test

Answer

To compare the means of more than two samples

Question 46

Which assumption is essential for the valid implementation of the Kruskal-Wallis test?

Accepted Answer

Independence of the samples

Answer

Normally distributed data

Answer

Equal variances across samples

Answer

Samples of equal size

Question 47

In non-parametric testing, which measure quantifies effect size in the chi-square test?

Accepted Answer

Cramer's V

Answer

Cohen's d

Answer

Eta squared

Answer

r

Question 48

Provide an example of a practical application of non-parametric statistics in the field of psychology.

Accepted Answer

Analyzing survey data with ordinal response categories

Answer

Comparing the efficacy of two distinct therapeutic approaches

Answer

Testing the significance of a correlation coefficient

Answer

Estimating the mean of a population

Question 49

Which of the following is an advantage of using non-parametric tests?

Accepted Answer

They make fewer assumptions about the data distribution.

Answer

They are always more powerful than parametric tests.

Answer

They can only be used with small sample sizes.

Answer

They are more difficult to interpret than parametric tests.

Question 50

Which test is appropriate for comparing the medians of two independent groups with non-parametric data?

Accepted Answer

Mann-Whitney U test

Answer

Kruskal-Wallis test

Answer

Chi-square test

Answer

Two-sample t-test

Question 51

How does increasing the sample size affect the power of a non-parametric test?

Accepted Answer

It increases the power.

Answer

It decreases the power.

Answer

It has no effect on the power.

Answer

It depends on the specific test being used.

Question 52

Which of the following is a limitation of non-parametric tests?

Accepted Answer

They can be less powerful than parametric tests.

Answer

They can be used to test hypotheses about means.

Answer

They require very large sample sizes.

Answer

They are always difficult to interpret.

Question 53

For two independent ordinal datasets in a psychological study, which non-parametric test is appropriate for comparing their distributions?

Accepted Answer

Mann-Whitney U Test

Answer

Chi-Square Test

Answer

Kruskal-Wallis Test

Question 54

When analyzing ordinal data in psychology research, what's a key advantage of using non-parametric tests?

Accepted Answer

Non-parametric tests require fewer assumptions about the data distribution.

Answer

Non-parametric tests are generally more powerful than parametric tests.

Answer

Non-parametric tests are simpler to calculate than parametric tests.

Answer

Non-parametric tests always provide more accurate results than parametric tests.

Question 55

When interpreting non-parametric hypothesis test results, why is it important to consider both p-values and effect sizes?

Accepted Answer

Both p-values and effect sizes provide a complete understanding.

Answer

P-values alone indicate statistical significance.

Answer

Effect sizes alone indicate practical importance.

Question 56

When using non-parametric tests, what ethical consideration is paramount?

Accepted Answer

Balancing statistical rigor with transparently reporting assumptions and limitations

Answer

Prioritizing statistical significance over ethical concerns

Answer

Avoiding non-parametric tests whenever possible

Question 57

Which of the following is not a defining characteristic of non-parametric statistics?

Accepted Answer

They make assumptions about the distribution of the data.

Answer

They are generally less powerful than parametric tests.

Answer

They are used when the data is not normally distributed.

Question 58

Which non-parametric test is used to compare two independent groups?

Accepted Answer

Mann-Whitney U test

Answer

Chi-square test

Answer

Kruskal-Wallis test

Answer

ANOVA (Analysis of Variance)

Question 59

What is the null hypothesis for a chi-square test of independence?

Accepted Answer

The two variables are independent.

Answer

The two variables are positively correlated.

Answer

The two variables are negatively correlated.

Answer

The two variables are normally distributed.

Question 60

What is the key difference between a parametric and a non-parametric test?

Accepted Answer

Parametric tests make assumptions about the distribution of the data, while non-parametric tests do not.

Answer

Parametric tests are more commonly used in research.

Answer

Parametric tests are more powerful than non-parametric tests.

Question 61

Which non-parametric test is used to compare three or more independent groups?

Accepted Answer

Kruskal-Wallis test

Answer

Chi-square test

Answer

Mann-Whitney U test

Answer

ANOVA (Analysis of Variance)

Question 62

What does the p-value in a non-parametric test represent?

Accepted Answer

The probability of obtaining a test statistic as extreme as or more extreme than the one observed, assuming the null hypothesis is true.

Answer

The probability of rejecting the null hypothesis.

Answer

The significance of the test result.

Question 63

Which of the following is an example of a non-parametric statistic?

Accepted Answer

Spearman's rank correlation coefficient

Answer

Student's t-test

Answer

Analysis of variance (ANOVA)

Answer

Pearson's correlation coefficient

Question 64

Which types of data are appropriate for non-parametric statistical tests?

Accepted Answer

Ordinal or nominal data

Answer

Interval data

Answer

Ratio data