Why is the Spearman rank correlation considered a non-parametric test?
- It assumes a normal distribution
- It can't handle ordinal data
- It does not assume a normal distribution
- It tests for a linear relationship
The Spearman rank correlation is considered a non-parametric test because it does not assume a normal distribution of data. It only assumes that the variables are ordinal or continuous and that the relationship between them is monotonic.
What are the degrees of freedom in a Chi-square test for a 2x3 contingency table?
- 2
- 3
- 4
- 6
In a Chi-square test, the degrees of freedom for a 2x3 contingency table is (2-1) * (3-1) = 2.
The process that aims to identify underlying variables, or factors, that explain the pattern of correlations within a set of observed variables is called _______.
- correlation analysis
- covariance analysis
- factor analysis
- regression analysis
The process that aims to identify underlying variables, or factors, that explain the pattern of correlations within a set of observed variables is called factor analysis.
If the Kruskal-Wallis H test is significant, it is often followed up with ________ to find which groups differ.
- ANOVA
- correlation analysis
- post hoc tests
- t-tests
If the Kruskal-Wallis H test is significant, it is often followed up with post hoc tests to find which groups differ. These tests are used to make pairwise comparisons between groups.
What are the implications of autocorrelation in the residuals of a regression model?
- It causes bias in the parameter estimates
- It indicates that the model is overfit
- It suggests that the model is underfit
- It violates the assumption of independent residuals
Autocorrelation in the residuals of a regression model violates the assumption of independent residuals. This can lead to inefficient estimates and incorrect standard errors, leading to unreliable hypothesis tests and confidence intervals.
How does increasing the sample size affect the power of a statistical test?
- Decreases the power
- Does not affect the power
- Increases the power
- May either increase or decrease the power
Increasing the sample size generally increases the power of a statistical test. This is because a larger sample provides more information, making it more likely that the test will detect a true effect if one exists.
How do post-hoc tests in ANOVA assist in interpreting the results?
- They help to adjust the level of significance
- They help to calculate the F statistic
- They help to check the assumptions of the ANOVA
- They help to determine which specific group means are significantly different from each other
Post-hoc tests in ANOVA help to determine which specific group means are significantly different from each other, after a significant overall ANOVA result. They control the overall Type I error rate across multiple comparisons.
A one-way ANOVA compares ________ group(s), while a two-way ANOVA compares ________ group(s).
- one; two
- three or more; two or more
- two; three
- two; two or more
A one-way ANOVA compares the means of three or more unrelated groups, while a two-way ANOVA compares the means of two or more groups that are split on two independent variables.
What are the potential disadvantages of using non-parametric statistical methods?
- They always give inaccurate results
- They can be less powerful than parametric tests when assumptions for parametric tests are met
- They cannot be used for certain types of data
- They cannot handle large data sets
Non-parametric statistical methods can be less powerful than parametric tests when the assumptions for the parametric tests are met. This is because they use less information (e.g., they use ranks rather than actual values). Therefore, if the data does meet the assumptions of parametric tests, parametric tests might be preferred.
Hypothesis testing in statistics is a way to test the validity of a claim that is made about a _______.
- Dataset
- Population
- Sample
- Statistic
In statistics, hypothesis testing is typically used to test claims about a population parameter, not a sample statistic, dataset, or an individual statistic.