# 140.611.94 STATISTICAL REASONING IN PUBLIC HEALTH I

Department:
Term: 3rd term
Credits: 3 credits
Contact: Felicity Turner
Description:

Provides a broad overview of biostatistical methods and concepts used in the public health sciences, emphasizing interpretation and concepts rather than calculations or mathematical details. Develops ability to read the scientific literature to critically evaluate study designs and methods of data analysis. Introduces basic concepts of statistical inference, including hypothesis testing, p-values, and confidence intervals. Topics include comparisons of means and proportions; the normal distribution; regression and correlation; confounding; concepts of study design, including randomization, sample size, and power considerations; logistic regression; and an overview of some methods in survival analysis. Draws examples of the use and abuse of statistical methods from the current biomedical literature.

Learning Objective(s):
Upon successfully completing this course, students will be able to:
Discuss and give examples of different types of data arising in public health studies
Interpret differences in data distributions via visual displays
Calculate standard normal scores and resulting probabilities
Calculate and interpret confidence intervals for population means and proportions
Interpret and explain a p-value
Perform a two-sample t-test and interpret the results; calculate a 95% confidence interval for the difference in population means
Use Stata to perform two sample comparisons of means and create confidence intervals for the population mean differences
Discuss and interpret results from Analysis of Variance (ANOVA), a technique used to compare means amongst more than two independent populations
Choose an appropriate method for comparing proportions between two groups construct a 95% confidence interval for the difference in population proportions
Use Stata to compare proportions amongst two independent populations
Discuss and interpret relative risks and odds ratios when comparing two populations
Discuss why survival (timed to event) data requires its own type of analysis techniques
Construct a Kaplan-Meier estimate of the survival function that describes the "survival experience" of a cohort of subjects
Interpret the result of a log-rank test in the context of comparing the "survival experience" of multiple cohorts
Interpret output from the statistical software package Stata related to the various estimation and hypothesis testing procedures covered in the course

Methods of Assessment: Assignment, mid-term and final examinations
Location: India
Enrollment Minimum: 5
Instructor Consent: No consent required
Auditors Allowed: Yes, with instructor consent