140.611.81 STATISTICAL REASONING IN PUBLIC HEALTH I
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.
After completion of this course, you will be able to do the following: 1) Understand and give examples of different types of data arising in public health studies 2) Interpret differences in data distributions via visual displays 3) Calculate standard normal scores and resulting probabilities 4) Calculate and interpret confidence intervals for population means and proportions 5) Interpret and explain a p-value 6) Perform a two-sample t-test and interpret the results; calculate a 95% confidence interval for the difference in population means 7) Use Stata to perform two sample comparisons of means and create confidence intervals for the population mean differences 8) Understand and interpret results from Analysis of Variance (ANOVA), a technique used to compare means amongst more than two independent populations 9) Choose an appropriate method for comparing proportions between two groups; construct a 95% confidence interval for the difference in population proportions 10) Use Stata to compare proportions amongst two independent populations 11) Understand and interpret relative risks and odds ratios when comparing two populations 12) Understand why survival (timed to event) data requires its own type of analysis techniques 13) Construct a Kaplan-Meier estimate of the survival function that describes the "survival experience" of a cohort of subjects 14) Interpret the result of a log-rank test in the context of comparing the "survival experience" of multiple cohorts 15) Interpret output from the statistical software package Stata related to the various estimation and hypothesis testing procedures covered in the course
Introduction to Online Learning is required prior to participating in any of the School's Internet-based courses..