# STATISTICAL REASONING IN PUBLIC HEALTH I Syllabus

140.611.11 | AY 2013-2014 - Summer Inst. Term | 3 Credit(s)
MTWThF 1:30:00 PM
• Contact Information
• Course Learning Objectives

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
• Course 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.

• Intended Audience

JHSPH students and Summer Institute participants

• Methods of Assessment

The code, discussed in the Policy and Procedure Memorandum for Students, March 31, 2002, will be adhered to in this class:

Students enrolled in the Bloomberg School of Public Health of The Johns Hopkins University assume an obligation to conduct themselves in a manner appropriate to the University's mission as an institution of higher education. A student is obligated to refrain from acts which he or she knows, or under the circumstances has reason to know, impair the academic integrity of the University. Violations of academic integrity include, but are not limited to: cheating; plagiarism; knowingly furnishing false information to any agent of the University for inclusion in the academic record; violation of the rights and welfare of animal or human subjects in research; and misconduct as a member of either School or University committees or recognized groups or organizations.

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