# Statistical Reasoning in Public Health I – JHSPH 140.611.81

This 2009 JHSPH OpenCourseWare graduate course is the first half of a course providing a broad overview of biostatistical methods and concepts used in the public health sciences, emphasizing interpretation and concepts rather than calculations or mathematical details. It develops ability to read the scientific literature, to critically evaluate study designs and methods of data analysis, and it 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. The course draws examples of the use and abuse of statistical methods from the current biomedical literature.

Learning Objectives

Upon completion of this course, you will be able to:

• understand 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.
• select an appropriate test for comparing two populations on a continuous measure, when the two sample t-test is not appropriate.
• understand 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.
• understand and interpret relative risks and odds ratios when comparing two populations.
• understand 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.

Course Organization

The content of this first of two courses is divided into eight lectures spread out over four "modules."  The four modules are:

• Module 1: Describing Data - Sessions 1 and 2
• Module 2: Confidence Intervals - Session 3
• Module 3: Comparing Two Groups and Hypothesis Testing - Sessions 4–7
• Module 4: Introduction to Survival Analysis - Session 8

PDF slides and audio recordings from lectures are available for each session. Additionally, practice problems and homework assignments were fortunately included with the associated OpenCourseWare course. A final exam was given for this course, but it is not available through OCW.

No text was originally required for this course. However, several books were listed as "useful, but optional":

* Altman, D. G. (1990). Practical Statistics for Medical Research. Boca Raton, Florida: CRC Press.

* Freedman, D., R. Pisani, and R. Purves. (2007). Statistics. 4th Edition. New York: W. W. Norton & Company, Inc.

* Moore, D., G. McCabe, and B. Craig. (2012). Introduction to the Practice of Statistics. 7th Edition. New York: W. W. Norton & Company, Inc.

Recommended reading was assigned with most sessions, always from Practical Statistics for Medical Research. Consult the Recommended Reading associated with each session.

Other Requirements

Students are also required to have access to Small Stata, a version of Stata that is less powerful (in terms of the amount of data it can store and process, not in terms of functionality) than regular Intercooled Stata, and costs significantly less. Small Stata carries a one-year users license. However, if you intend to further your study of statistics beyond this course, you may wish to purchase a copy of Intercooled Stata 8.

This flowchart should aid in choosing a correct statistical procedure during this course: Choosing a Correct Statistical Procedure

This document provides more information on performing a paired t-test in Stata: Paired T-test in Stata

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