Statistical Reasoning in Public Health provides an introduction to selected important topics in biostatistical concepts and reasoning through lectures, exercises, and bulletin board discussions. It 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.
This course is an adaptation of a course originally developed and taught by Ron Brookmeyer.
Syllabus
Course Description
Statistical Reasoning in Public Health provides an introduction to selected important topics in biostatistical concepts and reasoning through lectures, exercises, and bulletin board discussions. 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.
This course is an adaptation of a course originally developed and taught by Ron Brookmeyer.
Course Objectives
After completion of this course, you will be able to do the following:
- 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.
Readings
The required textbook for this course is . . .
- Altman, D.G. (1991). Practical Statistics for Medical Research. London: Chapman and Hall
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.
Other useful, but optional, references include the following:
- Freedman, D., Pisani, R., Purves, R. Statistics
- Moore, D., McCabe, G. Introduction to the Practice of Statistics
You may purchase any of these materials from Matthews Medical Book Center.
Course Topics
- Lecture 1: Describing Data, Part 1
- Lecture 2: Describing Data, Part 2
- Lecture 3: Confidence Intervals
- Special Lecture: Some Useful Stata Information
- Lecture 4: The Paired t-test and Hypothesis Testing
- Lecture 5: Comparing Means Among Two (or More) Independent Populations
- Lecture 6: Comparing Proportions Between Two Independent Populations
- Lecture 7: When Time Is of Interest: The Case for Survival Analysis
Course Format
The content of this course is divided into four separate modules. All the required course work can be accessed from the Course Modules page. The lecture sections are presented sequentially and should be completed in that order. Each of these sections combines audio presentation and slides - just like attending lectures in class. You may return to any previous section at any point and review its contents at your convenience. In each lecture section, you will find a listing of the section objectives, links to the lecture materials, a listing of reading assignments, and links to Web resources.
Schedule
| Welcome to Statistical Reasoning I |
Course Introductory Video |
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| Describing Data |
Introductory Video for Module 1Lecture 1
Lecture 2
Homework 1
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| Confidence Intervals |
Introductory Video for Module 2
Lecture 3
Homework 2
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Quiz 1 |
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| Comparing Two Groups and Hypothesis Testing |
Introductory Video for Module 3 |
| The Paired t-Test and Hypothesis Testing |
Lecture 4 |
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Homework 3 |
| Comparing Means Among Two (or More) Independent Populations |
Lecture 5 |
| Comparing Proportions Between Two Independent Populations |
Lecture 6 |
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Quiz 2 |
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| Introduction to Survival Analysis |
Introductory Video for Module 4 |
| When Time is of Interest: The Case for Survival Analysis |
Lecture 7 |
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Homework 4 |
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Final ExamThis is a proctored exam; you will receive detailed instructions from your professor about this process.
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Course Evaluation |