**This is the syllabus used in conjunction with educational content offered by JHSPH. As a result, some of the information and/or materials listed here may not be relevant to or available for an OCW user's self-directed study.**

# Syllabus

## Course Description

Introduction to Biostatistics provides an introduction to selected important topics in biostatistical concepts and reasoning. This course represents an introduction to the field and provides a survey of data and data types. Specific topics include tools for describing central tendency and variability in data; methods for performing inference on population means and proportions via sample data; statistical hypothesis testing and its application to group comparisons; issues of power and sample size in study designs; and random sample and other study types. While there are some formulae and computational elements to the course, the emphasis is on interpretation and concepts.## Course Objectives

Upon completion of the course, students are able to:

- Recognize and give examples of different types of data arising in public health and clinical 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
- 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
- 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
- Describe different kinds of studies
- Understand confounding and interaction in studies
- Use SPSS/STATA package to
- Perform two sample comparisons of means and create confidence intervals for the population mean differences
- Compare proportions amongst two independent populations
- Interpret output from the statistical software package STATA related to the various estimation and hypothesis testing procedures covered in the course