Statistics Quiz on ANOVA and t-tests

Which statistical test is appropriate when examining the difference between two groups with different participants per predictor level?

What research design requires the use of repeated measures ANOVA?

What is the key feature that distinguishes the use of an independent t-test from a dependent t-test?

Which statistical technique is best when analysing the relationship between multiple predictor variables, and a single outcome variable?

When does a researcher use a factorial ANOVA?

A study comparing the effectiveness of three different teaching methods on student test scores, with each student only experiencing one method, would be best analyzed using which statistical approach?

Which statistical technique is most appropriate when assessing the impact of height (measured in cm) and weight (measured in kg) on blood pressure (measured in mmHg)?

A researcher wants to understand the effect of a new drug dosage (measured in milligrams) on reaction time. All participants are given all the dosage levels, in a randomized crossover design. Which statistical approach is most suitable?

If a statistical analysis involves a predictor variable with two levels (e.g., treatment vs. control), and the same participants are measured at both levels, what type of design is this classified as?

Suppose you are investigating the relationship between anxiety levels (measured on a continuous scale), type of therapy (cognitive behavioral therapy, psychodynamic therapy), and the number of sessions participants attend. What would be the best statistical test to analyze data from this study?

Which condition in the analysis of variance (ANOVA) is not a core assumption that requires checking?

If you are analyzing the impact of a treatment (categorized as either 'yes' or 'no') and age (in years, a continuous variable) on memory scores, and all participants only have the treatment level, what approach do you use?

Which of the following is the key difference between independent and dependent ANOVA?

What is the first step in calculating both the Wilcoxon rank sum and Mann-Whitney U statistics?

After ranking the data, what is the next step in calculating both Wilcoxon and Mann-Whitney statistics?

What does the Wilcoxon statistic, WS, represent?

If two scores are tied, how are ranks assigned?

In the context of the provided information, what would indicate little difference between the groups?

How is the Mann-Whitney U statistic calculated using the sum of ranks for group 1 (R1)?

What is the formula for calculating the mean of WS, the Wilcoxon statistic, given group sizes of n1 and n2?

What is the formula for the standard error of WS (SEWS)?

If a study has two independent groups and the data does not meet the assumptions of parametric tests, which statistical test should be used?

What is the appropriate statistical test to determine correlation between two variables where the data does not meet the assumptions of a parametric test?

For a study with one factor, measured repeatedly across multiple time points, where the data meets parametric assumptions, which test should be used?

When should a Wilcoxon signed-rank test be used?

What statistical test should be used when comparing three or more independent levels from a single factor and the data does not meet the assumptions of parametric tests?

Which statistical test should be used when the outcome is categorical and the data does not meet assumptions for parametric tests?

If you have two factors being investigated, in an experimental design where both factors are manipulated and the data meets assumptions for parametric tests, what test should be used?

Which analysis is appropriate to analyze data from a study with a categorical dependent variable and multiple predictor variables, when parametric assumptions are not met?

When comparing two related samples, and the assumptions for parametric tests are met, what is the appropriate statistical test?

What statistical analysis is used in a study to account for a continuous variable influencing the outcome, while evaluating the effect of a single-factor manipulated independent variable on the same, continuous, dependent variable? Assume parametric assumptions are met.

Which statistical test is appropriate for analyzing the relationship between two categorical variables?

If you have two independent groups and want to compare their medians, which test would be appropriate?

Which statistical approach is suitable when you have a categorical outcome variable?

When you want to explore relationships between multiple categorical variables, which test is used?

Which of these is a non-parametric test equivalent to a one-way ANOVA?

If you have three or more related groups and you want to compare their distributions, which non-parametric test should you use?

What test would you use to analyze the relationship between two continuous variables?

When you have the classic ANOVA assumptions but want to control for a continuous covariate, which statistical procedure would you use?

In the context of the provided training data, what does the value '76' represent?

What is the purpose of calculating row totals in the context of this example?

What do column totals represent in the contingency table given?

Based on the information provided, what does $F_{Efood,yes}$ represent?

What does 'N' represent in the formula $F_{Efood,yes} = \frac{RowTotal_{yes}}{N} \times ColumnTotal_{food}$ ?

How is the expected frequency for a cell calculated under the null hypothesis?

What is the primary difference between the chi-square test ($\chi^2$) and the likelihood ratio test ($L\chi^2$) as presented?

In the formula for the chi-square test, what does $\Sigma$ symbolize?

Given the formula $ \chi^2 = \sum \frac{(O_{Fij} - E_{Fij})^2}{E_{Fij}} $, what would happen if the observed frequency ($O_{Fij}$) was exactly equal to the expected frequency ($E_{Fij}$) in any given cell?

How is the degrees of freedom (df) calculated in both chi-square and likelihood ratio tests?

What is the null hypothesis in the context of this experiment?

What is the first step in the presented decision tree?

What is the expected frequency of 'no dancing with affection as a reward', based on the provided expected values (EXP) in the table?

Within the chi-square formula as presented, what does the term $O_{Fij}$ represent?

In the context of the likelihood ratio test, what is the function of ‘ln’?

Continuous (CONT) variable

Categorical (CAT) variable

One-way ANOVA

Independent Samples t-test

Multiple regression

Repeated measures ANOVA

Paired Samples t-test

Two-way ANOVA

Paired Samples t-test (Dependent Samples t-test)

Factorial ANOVA

Pearson Correlation

Spearman Correlation

Mann-Whitney U Test

Kruskal-Wallis Test

Friedman Test

Wilcoxon Rank Sum Test

Sum of Ranks

WS Statistic

Mean of WS

Standard Error of WS

Two Independent Conditions

Average Rank

Chi-square test

Expected frequencies

Residuals

Null hypothesis

Observed frequencies

Alpha level

Contingency table

Likelihood ratio test

Decision tree

Degrees of freedom

Chi-square statistic

Proportion test

T-test

Correlation test

Main effect

ANCOVA

Wilcoxon Signed-Rank Test

Log-Linear Analysis

Logistic Regression