Safaa Dabagh

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Pre-Assessment

Module 10: Analysis of Variance (ANOVA)

About This Pre-Assessment

This short assessment measures your baseline knowledge of ANOVA concepts before you begin the module. Your responses help us understand:

What you already know about comparing multiple groups
Areas where the lessons can focus more attention
Your learning gains when compared to the post-assessment

Don't worry if you don't know the answers! This is completely normal. The goal is to establish a baseline, not to test you.

What is the main purpose of ANOVA (Analysis of Variance)?

A) To compare the means of exactly two populations B) To compare the means of three or more populations simultaneously C) To test if two categorical variables are independent D) To measure the correlation between two variables

Why shouldn't we use multiple two-sample t-tests instead of ANOVA when comparing 4 groups?

A) T-tests are too complicated to perform multiple times B) Multiple t-tests inflate the Type I error rate (α increases above 0.05) C) T-tests can only compare populations with equal variances D) ANOVA is faster to calculate than multiple t-tests

The F-statistic in ANOVA compares:

A) The largest mean to the smallest mean B) The variance between groups to the variance within groups C) The sample size of each group D) The p-value to the significance level

A researcher performs ANOVA and gets a significant result (p < 0.05). What does this tell us?

A) All group means are significantly different from each other B) At least one group mean is different from the others C) Exactly which pairs of groups differ significantly D) The first group has the highest mean

Which of the following is NOT an assumption for one-way ANOVA?

A) Observations are independent B) Populations are normally distributed C) All populations have equal variances D) All groups must have exactly the same sample size

Your responses are saved for research purposes to measure learning gains.

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