**About this course: **A conceptual and interpretive public health approach to some of the most commonly used methods from basic statistics.

OverviewSyllabusFAQsCreatorsRatings and Reviews## Statistical Reasoning for Public Health 1: Estimation, Inference, & Interpretation

Johns Hopkins University

**About this course: **A conceptual and interpretive public health approach to some of the most commonly used methods from basic statistics.

**Taught by:**John McGready, PhD, MS, Associate Scientist, Biostatistics

Commitment | 8 weeks of study, 2-3 hours/week |

Language | English |

How To Pass | Pass all graded assignments to complete the course. |

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Syllabus

WEEK 1

Introduction and Module 1

This module, consisting of one lecture set, is intended to whet your appetite for the course, and examine the role of biostatistics in public health and medical research. Topics covered include study design types, data types, and data summarization.

7 videos, 1 reading

**Video:**Welcome to the Course!**Reading:**Syllabus**Video:**Introduction to Module 1**Video:**Lecture 1A: The Role of Statistics in Public Health Research**Video:**Lecture 1B: Samples Versus Population**Video:**Lecture 1C: Considerations with Regard to Study Design**Video:**Lecture 1D: Data Types and Summarization**Video:**Lecture 1E: Self-Assessment/Active Learning Exercise

WEEK 2

Module 2A: Summarization and Measurement

Module 2A consists of two lecture sets that cover measurement and summarization of continuous data outcomes for both single samples, and the comparison of two or more samples. Please see the posted learning objectives for these two lecture sets for more detail.

10 videos, 6 readings, 5 practice quizzes

**Video:**Introduction to Module 2**Reading:**Learning Objectives, Lecture Set 2**Video:**Lecture 2A: Continuous Data: Useful Summary Statistics**Video:**Lecture 2B: Continuous Data: Visual Displays**Video:**Lecture 2C: Continuous Data: The Role of Sample Size on Sample Based Estimates**Video:**Lecture 2D: Continuous Data: Comparing Distributions**Video:**Lecture 2E: Self Assessment/Active Learning Exercise**Reading:**Learning Objectives, Lecture Set 3**Video:**Lecture 3A: The Standard Normal Distribution Defined**Video:**Lecture 3B: Applying the Principles of the Normal Distribution to Sample Data**Video:**Lecture 3C: What Happens When We Apply the Properties of the Normal Distribution to Data Not Approximately Normal: A Warning**Video:**Lecture 3D: Some Practice Exercises .**Reading:**Supporting Documents for Homework 1A**Practice Quiz:**Homework 1A**Practice Quiz:**Homework 1B**Practice Quiz:**Homework 1C**Reading:**Supporting Information for Homework 1D**Practice Quiz:**Homework 1D**Reading:**Supporting Information for Homework 1E**Practice Quiz:**Homework 1E**Reading:**Quiz 1 Solutions

WEEK 3

Module 2B: Summarization and Measurement

Module 2B includes a single lecture set on summarizing binary outcomes. While at first, summarization of binary outcome may seem simpler than that of continuous outcomes, things get more complicated with group comparisons. Included in the module are examples of and comparisons between risk differences, relative risk and odds ratios. Please see the posted learning objectives for these this module for more details.

6 videos, 1 reading

**Reading:**Learning Objectives, Lecture Set 4**Video:**Lecture 4A: Binary Data: Definition and Summarization (Binomial Distribution, P-Hat, SD)**Video:**Lecture 4B, part 1**Video:**Lecture 4B, part 2**Video:**Lecture 4C: Comparing Distributions of Binary Data: Odds Ratios**Video:**Lecture 4D: A Brief Note About Ratios**Video:**Lecture 4E: Self Assessment/Active Learning Exercise

WEEK 4

Module 2C: Summarization and Measurement

This module consists of a single lecture set on time-to-event outcomes. Time-to-event data comes primarily from prospective cohort studies with subjects who haven to had the outcome of interest at their time of enrollment. These subjects are followed for a pre-established period of time until they either have there outcome, dropout during the active study period, or make it to the end of the study without having the outcome. The challenge with these data is that the time to the outcome is fully observed on some subjects, but not on those who do not have the outcome during their tenure in the study. Please see the posted learning objectives for each lecture set in this module for more details.

6 videos, 5 readings, 6 practice quizzes

**Reading:**Learning Objectives, Lecture Set 5**Video:**Lecture 5A: Time to Event Data: Definition (Censoring) and Numerical Summary Measures (Incidence Rates)**Video:**Lecture 5B: Numerically Comparing Groups on Time to Event Outcomes**Video:**Lecture 5C Part 1 Time to Event Data: Graphical Summarization: Kapalan-Meier Approach**Video:**Lecture 5C Part 2 Time to Event Data: Graphical Summarization: Kapalan-Meier Approach**Video:**Lecture 5D: Graphically Comparing Groups on Time to Event Outcomes**Video:**Lecture 5E: Self Assessment/Active Learning Exercise**Reading:**Supporting Documents For Homework 2A**Practice Quiz:**Homework 2A**Practice Quiz:**Homework 2B**Reading:**Supporting Information for Homework 2C**Practice Quiz:**Homework 2C**Practice Quiz:**Homework 2D**Practice Quiz:**Homework 2E**Practice Quiz:**Homework 2F**Reading:**Formula Files for Quiz 2**Reading:**Quiz 2 Solutions

WEEK 5

Module 3A: Sampling Variability and Confidence Intervals

Understanding sampling variability is the key to defining the uncertainty in any given sample/samples based estimate from a single study. In this module, sampling variability is explicitly defined and explored through simulations. The resulting patterns from these simulations will give rise to a mathematical results that is the underpinning of all statistical interval estimation and inference: the central limit theorem. This result will used to create 95% confidence intervals for population means, proportions and rates from the results of a single random sample.

11 videos, 2 readings

**Video:**Introduction to Module 3**Reading:**Learning Objectives, Lecture Set 6**Video:**Lecture 6A: Sampling Distribution Definition**Video:**Lecture 6B: Examples: Sampling Distribution for a Single Mean**Video:**Lecture 6C: Examples: Sampling Distribution for a Single Proportion, Incidence Rate**Video:**Lecture 6D: Estimating Sampling Distribution Characteristics from Single Samples of Data**Video:**Lecture 6E: Self Assessment/Active Learning Exercise**Reading:**Learning Objectives, Lecture Set 7**Video:**Lecture 7A: Confidence Intervals for Population Means**Video:**Lecture 7B: Confidence Intervals for Sample Proportions and Rates**Video:**Lecture 7C: On the Interpretation of CIs**Video:**Lecture 7D: A Note about CIs for Smaller Samples/Exact CIs**Video:**Lecture 7E: Self-Assessment/Active Learning Exercise

WEEK 6

Module 3B: Sampling Variability and Confidence Intervals

The concepts from the previous module (3A) will be extended create 95% CIs for group comparison measures (mean differences, risk differences, etc..) based on the results from a single study.

7 videos, 3 readings, 3 practice quizzes

**Reading:**Learning Objectives, Lecture Set 8**Video:**Lecture 8A: An Overview of Confidence Intervals for Population Comparison Measures**Video:**Lecture 8B: Confidence Intervals for Differences in Population Means**Video:**Lecture 8C: Confidence Intervals for Binary Comparisons: Part 1, Difference in Proportions (Risk Difference) - Subtitles Pending**Video:**Lecture 8D: Confidence Intervals for Binary Comparisons: Part 2: Ratio of Proportions (Relative Risk), Odds Ratio - Subtitles Pending**Video:**Lecture 8E: Confidence Intervals for Incidence Rate Ratios**Video:**Lecture 8F: Revisiting Ratios and the Log Scale (with Respect to Effect Sized and CIs)**Video:**Lecture 8G: Self Assessment/Active Learning Exercise**Reading:**Supporting Information For Homework 3A**Practice Quiz:**Homework 3A**Practice Quiz:**Homework 3B**Practice Quiz:**Homework 3C**Reading:**Quiz 3 Solutions

WEEK 7

Module 4A: Making Group Comparisons: The Hypothesis Testing Approach

Module 4A shows a complimentary approach to confidence intervals when comparing a summary measure between two populations via two samples; statistical hypothesis testing. This module will cover some of the most used statistical tests including the t-test for means, chi-squared test for proportions and log-rank test for time-to-event outcomes.

10 videos, 2 readings

**Video:**Introduction to Module 4**Reading:**Learning Objectives, Lecture Set 9**Video:**Lecture 9A: Two-Group Hypothesis Testing: The General Concept**Video:**Lecture 9B: Comparing Means between Two Populations: The Paired Approach**Video:**Lecture 9C: Comparing Means between Two Populations: The Unpaired Approach**Video:**Lecture 9D: Section D: Debriefing on the p-value, Part 1**Video:**Lecture 9E: Self Assessment Exercise**Reading:**Learning Objectives, Lecture Set 10**Video:**Lecture 10A: Comparing Proportions between Two Populations: The “Z-Test” Approach**Video:**Lecture 10B: Comparing Proportions between Two Populations: Chi-Squared and Fisher’s Exact Tests**Video:**Lecture 10C: Comparing Time-to-Event Between Two Populations: The Log-Rank Test**Video:**Lecture 10D: Debriefing on the P-Value, Part II

WEEK 8

Module 4B: Making Group Comparisons: The Hypothesis Testing Approach

Module 4B extends the hypothesis tests for two populations comparisons to "omnibus" tests for comparing means, proportions or incidence rates between more than two populations with one test

11 videos, 5 readings, 5 practice quizzes

**Reading:**Learning Objectives, Lecture Set 11**Video:**Lecture 11A: (Hypothesis Testing) Comparing Means Between More than Two Populations: Analysis of Variance (ANOVA)**Video:**Lecture 11B: (Hypothesis Testing) Comparing Proportions between More than Two Populations: Chi-Square Tests**Video:**Lecture 11C: Hypothesis Testing) Comparing Survival Curves between More than Two Populations: Log-Rank Tests**Reading:**Supporting Documents For Homework 4**Practice Quiz:**Homework 4A**Practice Quiz:**Homework 4B**Practice Quiz:**Homework 4C**Practice Quiz:**Homework 4D**Practice Quiz:**Homework 4E**Reading:**Quiz 4 Solutions**Reading:**Learning Objectives, Lecture Set 12**Video:**Lecture 12A: Precision and Sample Size : An Overview**Video:**Lecture 12B: Computing Sample Size to Achieve a Desired Level of Precision : Single Population Quantities**Video:**Lecture 12C: Computing Sample Size to Achieve a Desired Level of Precision : Population Comparison Quantities**Reading:**Learning Objectives, Lecture Set 13**Video:**Lecture 13A: Power and Its Influences**Video:**Lecture 13B: Sample Size Computations For Studies Comparing Two (or More) Means**Video:**Lecture 13C: Sample Size Computations For Studies Comparing Two (or More) Proportions or Incidence Rates**Video:**Lecture 13D: Sample Size and Study Design Principles: A Brief Summary**Video:**Lecture 13E: An Example of the Mathematics Behind Power Computations

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Johns Hopkins University

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Ratings and Reviews

Rated 4.8 out of 5 of 146 ratings

It is an awesome course which does not require much math background and provides clear logic behind the interpretation of statistic results from the public health area. Strongly recommend!

EM

Incredibly well organized and easy to understand. Would recommend to other researchers!

The course is excellent. It really reflects its name, statistical reasoning. The instructor is awesome as well.

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ps

great statics course