Topics List: Exam 2, Spring 2026

Material covered

  • Lectures 8-15, HW 4-6, Labs 4-6, AE 2-4

Coding

  • pnorm() and qnorm() and pt() and qt()

  • slice() and names() and getwd() and summarize()

  • mutate() to create new variables, select() to select variables, and arrange() to arrange

  • Review using ggplot() to create various visualizations, such as boxplots, histograms, scatterplots etc. Also geom_sf() for maps.

  • What is the sf package used for? When would you use it?

  • st_read()

  • What are some examples of metadata?

  • Be able to read/understand code using R functions for ANOVA, t-tests, power: aov(), t.test(), power.t.test, summary() (used as summary(aov(y~group))).

  • Be able to recognize good and bad coding style in example code, and be able to suggest a correction. Topics include: proper spacing, curly braces, long lines, pipes, layering in ggplot2()

Conceptual and Applied

  • What’s the basic idea behind the Central Limit Theorem (CLT)? Why does it matter?

  • Be able to find approximate distribution (based on CLT) of sample averages given a population mean and standard deviation. (IQ example in Lecture 8)

  • What is the rule of thumb (sample size) where CLT kicks in?

  • What is a confidence interval? What is the basic form of a confidence interval?

  • What is a confidence multiplier? How to find confidence multiplier in R using qnorm()?

  • What is a margin of error?

  • How do you correctly interpret a confidence interval? Why can’t we say “There’s a 95% chance that…”? Know how to interpret a confidence interval, (asthma and SBP examples in Lecture 9).

  • How does the t distribution relate to the normal distribution? How are they different? Why would we use the t-distribution instead of the Z?

  • How many parameters does the t-distribution have?

  • Know how to find the cumulative probability and quantiles from a t-distribution using qt() and pt().

  • What is p-hacking?

  • Basic steps to hypothesis testing framework

  • Know how to write null and alternative hypotheses in words and symbols

  • Definition of a p-value

  • Interpret t.test output in R

  • Definitions of Type 1 and Type 2 errors. Name each type of error for a given context (e.g. pregnancy test or HIV test, identify which type of error might be more severe and you would therefore want to minimize)

  • Dangers of using a one-sided test

  • Independent sample t-test vs. paired/matched t-test

  • When would you use a pooled variance estimate vs. a Satterthwaite approximation?

  • What type of research question can you answer with an ANOVA? State null and alternative hypotheses, and assumptions of ANOVA.

  • What is the issue of “multiple comparisons”? Why do we want to avoid them? What is a Bonferroni correction?

  • What is the basic idea of an F-test in ANOVA? (What is it testing?) How do you get the numerator and denominator degrees of freedom? Be able to interpret R output from doing an F-test (pet data example in Lecture 12).

  • What is a chi-square test for independence of two categorical variables? State null and alternative hypotheses and assumptions. What are the degrees of freedom? What are “expected” vs. “observed” counts?

  • When would we use a Fisher exact test instead of a chi-square test?

  • At a high level, what is the advantage of nonparametric hypothesis testing vs. parametric testing?

  • What are the nonparametric alternatives to paired t-test, independent samples t-test, ANOVA?

  • What’s the definition of power?

  • When we vary sample size, variance, alpha, and minimum detectable difference, how is power affected?

  • How to use power.t.test() to calculate sample size and power.