Yr 11 Ch8

Which of the following is an advantage of Spearman's rank correlation coefficient over Pearson's correlation coefficient?

• Requires linear data
• Cannot be used on ranked data
• Is greatly affected by outliers
• Can be used on data that is not linear

Spearman's rank correlation coefficient is most suitable for which type of data?

• Nominal data
• Linearly related data
• Data with outliers or non-linear relationships
• Data that shows perfect correlations

What is the limitation of Spearman's rank correlation coefficient when the dataset is small?

• It perfectly reflects the strength of the relationship
• It can be overly affected by outliers
• It may not accurately reflect the relationship's strength
• It cannot be calculated with a small dataset

The chi-squared test for independence is inappropriate when:

• Expected frequencies are greater than 5
• There's a small sample size with expected frequencies less than 5
• Data is on an interval scale
• There is a linear relationship between variables

What is a limitation of the chi-squared test?

• It can be used for any sample size
• It can be applied to continuous data
• It assumes that variables are normally distributed
• It requires that expected frequencies be at least 5

Why are degrees of freedom important in a chi-squared test?

• They determine the non-linearity of the data
• They indicate the sample size needed for the test
• They affect the calculation of expected frequencies
• They relate to the number of independent comparisons being made

A t-test is inappropriate for comparing two datasets when:

• The data are on different scales
• The data are normally distributed
• One dataset is much larger than the other
• The data is not normally distributed

Which assumption does the t-test rely on that can be considered a limitation?

• Data must show a strong correlation
• Data must be ranked
• Data must be normally distributed
• Data must have equal variances

When comparing two sets of data using a t-test, what is a potential limitation if the data have unequal variances?

• The test becomes more accurate
• The test can still be used without any issues
• It could lead to incorrect conclusions
• The test will automatically adjust for differences

Which of the following is a pro of using Spearman's correlation?

• It is sensitive to outliers
• It requires the data to be on an interval scale
• It works well with non-monotonic data
• It assumes normal distribution of data