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Methods in Biostatistics II

Course Status

2nd Term
Academic Year
2022 - 2023
Instruction Method
Synchronous Online
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Contact Name
Frequency Schedule
One Year Only


Continues studying hypothesis testing and estimation concepts in the second part of the course. Includes the following topics: (1) power of tests, (2) P-value and its properties; (3) Binomial proportion; Wald test; Score test; exact test; (4) Two-sample binomial; odds ratio (OR), relative risk (RR), risk difference (RD); (5) Hypergeometric distribution and Fisher’s exact test; (6) Confounding; stratification; Mantel-Haenszel estimator; (7) More on OR, RR, RD; Simpson’s paradox; collapsibility; unmeasured confounding; E-value; (8) Case-control; Matched case-control; McNemar’s test; (9) Logistic regression; non-differential and differential measurement error; (10) Goodness of fit tests and Chi-squared test for contingency tables; (11) Nonparametric statistics; (12) ANOVA: one-way and two-way; (13) Multivariate distributions; conditional expectation; Linear regression
Learning Objectives
Upon successfully completing this course, students will be able to:
  1. Discuss core applied statistical concepts and methods
  2. Discuss the display and communication of statistical data
  3. Describe the distinctions between the fundamental paradigms underlying statistical methodology
  4. List the basics of maximum likelihood
  5. List the basics of frequentist methods: hypothesis testing, confidence intervals
  6. Discuss the creation and interpretation of P values
  7. Describe estimation, testing and interpretation for single group summaries such as means, medians, variances, correlations and rates
  8. Describe estimation, testing and interpretation for two group comparisons such as odds ratios, relative risks and risk differences
  9. Describe the basic concepts of ANOVA
  10. Describe the basic ideas of nonparametric methods
  11. Describe the basic ideas behind linear regression
Methods of Assessment
This course is evaluated as follows:
  • 40% Homework
  • 20% Midterm
  • 10% Final Project
  • 30% Final Exam
Special Comments

Students will choose only one of the two lab times.