24 Questions
What is the primary consideration in selecting an appropriate inferential statistic for a two-variable question or hypothesis, assuming both variables are normal/scale?
The level (scale) of measurement of both variables.
When would you use the Mann Whitney U test in a between-groups design?
When the dependent variable is ordinal and the parametric assumptions are markedly violated.
What is the assumption of variance homogeneity in ANOVA, and why is it important?
The assumption of variance homogeneity in ANOVA is that the variance of the dependent variable is equal across all groups, and it's important because it affects the accuracy of the test results.
When would you use the Friedman test in a within-subjects design?
When the dependent variable is ordinal and the parametric assumptions are markedly violated, and there are three or more levels of the independent variable.
What is the difference between a parametric test and a non-parametric test?
Parametric tests assume normality and equal variances, while non-parametric tests do not make these assumptions and are used for ordinal or non-normal data.
How do you determine if a dependent variable approximates normality?
By checking if the data approximate a normal distribution, either visually or through statistical tests.
What is the purpose of the paired samples t-test?
To compare the means of two related or paired groups, such as before and after measurements.
When would you use the Kruskal Wallis H test in a between-groups design?
When the dependent variable is ordinal and the parametric assumptions are markedly violated, and there are three or more groups.
What is a key assumption of parametric tests that is not required for non-parametric tests?
Normality of data
Which non-parametric test is used to measure the correlation between two variables?
Spearman's rank correlation
What is the assumption of variance homogeneity in statistical testing?
Equal variances across all groups
When is it necessary to use non-parametric tests instead of parametric tests?
When data does not meet the assumptions of parametric tests
What is the purpose of checking for linear relationships in statistical analysis?
To identify patterns and correlations between variables
Which non-parametric test is used to compare the median of two independent groups?
Mann Whitney U test
What is the difference between a parametric and non-parametric test?
Parametric tests require certain assumptions, non-parametric do not
When can parametric tests be used?
When data meets the assumptions of parametric tests
What is the main difference between parametric and non-parametric tests in terms of the type of data they can be applied to?
Parametric tests are applied to interval and ratio data, while non-parametric tests are applied to nominal and ordinal data.
What is the assumption of normality in parametric tests, and why is it important?
The assumption of normality states that the data should have a normal distribution or be symmetric. This is important because parametric tests are sensitive to deviations from normality.
What is the purpose of checking for homogeneity of variances in parametric tests?
To ensure that the data from multiple groups have the same variance, which is an assumption of parametric tests.
What is the importance of linearity in parametric tests?
Linearity assumes that the data have a linear relationship, which is necessary for some parametric tests like multiple regression.
What is the main advantage of non-parametric tests over parametric tests?
Non-parametric tests do not assume normality or equal variances, making them more robust to deviations from these assumptions.
When would you choose to use a non-parametric test over a parametric test?
When the data is ordinal or nominal, or when the assumptions of parametric tests are violated.
What is an example of a parametric test that assumes linearity?
Multiple regression assumes a linear relationship between the variables.
What is the main limitation of non-parametric tests?
They are less powerful than parametric tests, which means they require larger sample sizes to detect significant effects.
Learn how to select the appropriate inferential statistic for basic two-variable difference questions or hypotheses based on the scale of measurement of the dependent variable and the number of levels or groups of the independent variable.
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