Section Name Description
Module 1: Lesson 1: Introduction to Biostatistics URL Role of Biostatistics (12 mins)
  • Read the entire web page and watch the two videos. A transcript of each video is available.
  • Explore the basic principles of statistics and some of its common uses. 
URL Johns Hopkins Bloomberg School of Public Health – Statistics for laboratory scientists I (5 mins)

  • Read down from the slide titled "What is statistics?" (page 7 in the Adobe Toolbar).
  • Understand the basic principles of probability, descriptive statistics, and data analysis.

URL Basic statistical tools in research and data analysis (22 mins)
  • Read down to the beginning of the subsection titled "Parametric and Non-Parametric Tests". 
  • The rest of the article is considered supplemental reading. Ensure you click on the link to the erratum for the corrected version of Table 2.
  • Understand the basic principles of probability, descriptive statistics, and data analysis.

URL NHANES descriptive statistics

    Click on the titles: Checking frequency distribution and normality, Percentiles, Means, and Proportions and read the content. Once you finish, click on the task titles and read its content. You only need to read 1 task for each descriptive statistic title.

Module 1: Lesson 2: Types of Variables, Plots, and Graphs URL Levels of Measurement (15 mins)

  • Read the entire web page.
  • Understand the different types of variables, how they are used, and how to summarize the data. 

URL Introductory Statistics (80 mins)

  • Download the PDF version of the text by clicking on the appropriate link. Then, read sections 2.1, 2.2 (pertaining to histograms only), 2.3, and 2.4 (pages 68–100).  If your understanding is weak, go through the exercises.
  • Understand and identify the different types of plots and graphs.

URL Chapter 4 – Exploratory Data Analysis (20 mins)

  • Read section 4.3, "Univariate graphical EDA" (pages 72–88), as well as subsection 4.5.2, "Scatterplot" (pages 95–97).
  • Understand and identify the different types of plots and graphs.

URL Chapter 2 – Defining and Classifying Data Variables (20 mins)

  • Read the entire chapter (pages 917).
  • Understand the different types of variables, how they are used, and how to summarize the data. 

URL Types of Variables (14 mins)

  • Watch all 3 videos.  A transcript of each video is available.

Module 1: Lesson 3: Descriptive Statistics and Distribution URL Chapter 4 – Exploratory Data Analysis (25 mins)

  • Read section 4.2, "Univariate non-graphical EDA" (pages 63–72). Then, read part of section 4.4, "Multivariate non-graphical EDA" (from page 88 through page 90). 
  • Generate descriptive statistics from data, calculate descriptive statistics and standard deviations, and understand the methods of summarizing a single quantitative variable.     

URL One Categorical Variable (13 mins)

  • Read the entire web page.
  • Summarize and describe the distribution of a categorical variable and understand the uses and implications of the normal distribution.

URL Statistics by Example (35 mins)

  • Click on the link “Lecture Handouts (pdf, ppt)" found on the left side of the web page.  Then, under “Lecture Handouts”, click on the link “Chapter 4 Displaying, Summarizing Quantitative Data (pdf)” and read the entire handout.
  • Summarize and describe the distribution of a categorical variable and understand the uses and implications of the normal distribution.

URL 4.2 – The Normal Curve (10 mins)

  • Read down to the beginning of Example 4.5. Ignore any information relating to z-scores for now.
  • Summarize and describe the distribution of a categorical variable and understand the uses and implications of the normal distribution.

URL Introductory Statistics (45 mins)

  • Download the PDF version of the text by clicking on the appropriate link. Then, read sections 2.5–2.7 (pages 100–120).

Module 1: Lesson 4: Data Analysis and Study Design URL Dr. Elizabeth Newton – 15.075, Applied Statistics (15 mins)

  • Read all slides.
  • Understand the basic types of data, the main ways in which data are used, and important considerations when using data in analysis.

URL Importance of using basic statistics adequately in clinical research (26 mins)

  • Read the entire article. It provides an overview of the entire course.
  • Understand the basic types of data, the main ways in which data are used, and important considerations when using data in analysis.

URL Webinar Using Data to Guide and Evaluate Responses to the Opioid Crisis Rhode Island's Drug Overdose
Watch the entire webinar. The webinar deals with how the timely analysis and public dissemination of data being used to guide and evaluate policy and public health response to the overdose crisis in Rhode Island. (60 minutes)
URL 2.7 – Statistical Ethics (2 mins)

  • Read the entire web page.

Module 2: Lesson 1: Probability, Frequency, and the Concepts of Probability URL Introduction to Probability (30 mins)

  • Read the entire web page. Attempt any questions if understanding is weak.
  • Relate the probability of an event to the likelihood of this event occurring.

URL Introductory Statistics (25 mins)

  • Download the PDF version of the text by clicking on the appropriate link. Then, read section 1.3, "Frequency, Frequency Tables, and Levels of Measurement" (pages 26–35).
  • Understand how to interpret and generate proportions from data.

URL Basic Probability Rules (40 mins)

  • Read the entire web page. Attempt any questions if understanding is weak.
  • Explain how relative frequency can be used to estimate the probability of an event.

URL Introductory Statistics (40 mins)

  • Download the PDF version of the text by clicking on the appropriate link. Then, read sections 3.1 and 3.2, titled "Terminology" and "Independent and Mutually Exclusive Events", respectively (pages 176–188).
  • Understand the concepts of probability, conditional probability, and independence.

URL Johns Hopkins Bloomberg School of Public Health – Probability (15 mins)

  • Read the entire set of slides.

URL Johns Hopkins Bloomberg School of Public Health – Probability Concepts (35 mins)

  • Read to the end of slide 34. Attempt all relevant questions.

Module 2: Lesson 2: Variables, Sampling, and Distribution URL Unit 3B: Random Variables (80 mins)

  • Read the entire web page. Then, under the tab "Unit 3B: Random Variables" found on the left side of the web page, click on the links titled "Discrete Random Variables", "Binomial Random Variables", and "Continuous Random Variables", and read the content of those links.
  • Understand the concept of random variables.

URL 3.2.2 – Binomial Random Variables (15 mins)

  • Read the entire web page.
  • Understand the concept of random variables.

URL Sampling (30 mins)

  • Read the entire web page. Attempt any questions if understanding is weak.
  • Distinguish between samples and population and identify different types of samples.

URL Inferential Statistics (15 mins)

  • Read the entire web page.
  • Distinguish between samples and population and identify different types of samples.

URL Chapter 3 - Review of Probability (105 mins)

  • Read the entire chapter (pages 19–60). Focus on sections 3.1, 3.4, 3.5, 3.7, 3.8, and 3.9.
  • Understand sampling distribution, variance, and the central limit theorem.

URL Introduction to Sampling Distributions (20 mins)

  • Read the web page. Then, access the lesson "Sampling Distribution of the Mean" by clicking on the link titled "Standard" found on the left side of the web page under the heading "6. Sampling Distribution of the Mean" in the section titled "Chapter IX. Sampling Distribution". Read the content of that link.
  • Understand sampling distribution, variance, and the central limit theorem.

URL Central Limit Theorem (11 mins)

  • Read the entire web page.
  • Understand sampling distribution, variance, and the central limit theorem.

URL The Sampling Distribution (10 mins)

  • Read the entire article.
  • Understand sampling distribution, variance, and the central limit theorem.

URL Normal Random Variables (80 mins)

  • Read the web page, as well as the next three web pages titled "Standard Normal Distribution", "Normal Applications", and "Summary (Unit 3B – Random Variables)". Access the next three web pages by either clicking on the links found on the left side of the web page under the heading "Normal Random Variables" or by clicking on the link "Next" found at the end of the text on the right-hand side of the web page.
  • Understand the implications and uses of normality and skewness.

URL 1.5.1 – Measures of Central Tendency (30 mins)

  • Read the web page, as well as the next two web pages, "1.5.2 – Measures of Position" and "1.5.3 – Measures of Variability". Access lessons 1.5.2 and 1.5.3 by either clicking on the link found on the left side of the web page under the heading "Lesson 1" or by clicking on the links "1.5.2 – Measures of Position" and "1.5.3 – Measures of Variability" found at the end of the text on the bottom right-hand side of the web page.
  • Understand the implications and uses of normality and skewness.

Module 3: Lesson 1: Point and Confidence Interval Estimation URL Introduction to Estimation (30 mins)

  • Read the web page. Then, access the lessons "Degrees of Freedom" and "Characteristics of Estimators" by clicking on the links titled "Standard" found on the left side of the web page under the headings "3. Degrees of Freedom" and "4. Characteristics of Estimators" in the section "Chapter X. Estimation". Read the content of those links.
  • Understand and be able to apply point estimation and confidence interval estimation.

URL 4.2 – Introduction to Confidence Intervals (7 mins)

  • Read the web page, as well as the content of sections 4.2.1 (Interpreting Confidence Intervals) and 4.2.2 (Application of Confidence Intervals). Access sections 4.2.1 and 4.2.2 by clicking on the appropriate links found on the left side of the web page.
  • Understand and be able to apply point estimation and confidence interval estimation.

URL Introductory Statistics (60 mins)

  • Download the PDF version of the text by clicking on the appropriate link. Then, read the introductory section to Chapter 8, as well as sections 8.1 to 8.3 (pages 443–467).  Sections 8.4 to 8.6 are considered optional reading.
  • Recognize inference on means versus inference on proportion.

URL Module 5: Interval Estimation (9 mins)

  • Read slides 2–8.
  • Be able to calculate one-sided and two-sided confidence intervals for mean and proportion.

URL Basic Concepts of Inference (60 mins)

  • Read to the end of slide 24.
  • Be able to calculate one-sided and two-sided confidence intervals for mean and proportion.

URL 9.1 – Confidence Intervals for a Population Proportion (13 mins)

  • Read the web page, as well as the content of section 9.2 (Confidence Intervals for a Population Mean). Access section 9.2 by clicking on the link "9.2 – Confidence Intervals for a Population Mean" found on the left side of the web page.

Module 3: Lesson 2: Effect of Sample Size on Confidence Interval URL Power and Sample Size Determination (30 mins)

  • Read the web page. Then, click on pages 2, 3, and 4, and read the content of each page.
  • Understand the effect of sample size and other conditions on estimation as well as understand and be able to calculate the sample size needed to achieve the desired confidence level.

URL Confidence Intervals Introduction (30 mins)

  • Read the web page. Then, on the left hand side of the web page, click on the link titled "Standard" found under each of the following six headings: "8. Confidence Interval for Mean", "9. t distribution", "10. Confidence Interval Simulation", "11. Difference between Means", "12. Correlation", and "13. Proportion". Read the content of those web pages.
  • Understand the effect of sample size and other conditions on estimation as well as understand and be able to calculate the sample size needed to achieve the desired confidence level.

Module 4: Lesson 1: Principles of Hypothesis Testing URL S.3 Hypothesis Testing (45 mins)

  • Read the web page, as well as the content of sections S.3.1 to S.3.3. Access sections S.3.1, "Hypothesis Testing (Critical value approach)", S.3.2, "Hypothesis Testing (P-Value Approach)", and S.3.3, "Hypothesis Testing: Examples" by clicking on the titled links found on the left side of the web page.
  • Reinforce understanding of probability distributions in the context of hypothesis testing, especially in drawing conclusions and types of errors.

URL Significance Testing (15 mins)

  • Read the web page. Then, on the left-hand side of the web page, click on the link titled "Standard" found under the heading "4. Type I and Type II Errors". Read the content of that web page.
  • Reinforce understanding of probability distributions in the context of hypothesis testing, especially in drawing conclusions and types of errors.

URL 1.1.2 – Explanatory and Response Variables (3 mins)

  • Read the entire web page.
  • Distinguish types of explanatory and response variables.

URL 10.1 – Setting the Hypotheses: Examples (9 mins)

  • Read the entire web page.
  • Distinguish types of explanatory and response variables.

URL Significance Testing and Confidence Intervals (7 mins)

  • Read the entire web page.
  • Understand the relationship between confidence interval and hypothesis testing.

URL Biostatistics Series Module 2: Overview of Hypothesis Testing (26 mins)

  • Read from the beginning of the article to the beginning of the section titled “Question 1. Is there a difference between groups or data sets that are unpaired (parallel or independent)?”.

URL 7.1.5. What is the relationship between a test and a confidence interval? (2 mins)

  • Read the web page.

Module 4: Lesson 2: Applications of Hypothesis Testing URL Introductory Statistics (60 mins)

  • Download the PDF version of the text by clicking on the appropriate link. Then, in Chapter 9, "Hypothesis Testing with One Sample", read the introductory page as well as sections 9.1 to 9.5 (pages 505–530).
  • Understand and apply hypothesis tests for a single mean and a single proportion as well as for two means (independent and paired/matched samples), and understand chi-squared test and ANOVA.

URL Introductory Statistics (30 mins)

  • Download the PDF version of the text by clicking on the appropriate link. Then, in Chapter 10, "Hypothesis Testing with Two Samples", read sections 10.1 and 10.2  (pages 568–579). Work through the exercises to strengthen your understanding.
  • Understand and apply hypothesis tests for a single mean and a single proportion as well as for two means (independent and paired/matched samples), and understand chi-squared test and ANOVA.

URL Introductory Statistics (40 mins)

  • Download the PDF version of the text by clicking on the appropriate link. Then, in Chapter 10, "Hypothesis Testing with Two Samples", read section 10.3 to the end of Chapter 10 (pages 579–590). Work through the exercises to strengthen your understanding.
  • Understand and apply hypothesis tests for a single mean and a single proportion as well as for two means (independent and paired/matched samples), and understand chi-squared test and ANOVA.

URL 8.1 – The Chi-Squared Test for Independence (26 mins)

  • Read the entire web page.
  • Understand and apply hypothesis tests for a single mean and a single proportion as well as for two means (independent and paired/matched samples), and understand chi-squared test and ANOVA.

URL Lesson 10: Introduction to ANOVA (30 mins)

  • Read the web page, as well as the entire content of sections 10.1 (Introduction to Analysis of Variance), 10.2 (A Statistical Test for One-Way ANOVA), 10.2.1 (ANOVA Assumptions), 10.2.2 (The ANOVA Table), 10.3 (Multiple Comparisons), 10.4 (Two-Way ANOVA), and 10.5 (Summary). Access each section in Chapter 10 by clicking on the titled links found on the left side of the web page under the heading "Lesson 10: Introduction to ANOVA".
  • Understand and apply hypothesis tests for a single mean and a single proportion as well as for two means (independent and paired/matched samples), and understand chi-squared test and ANOVA.

URL 6. Statistical Inference and Hypothesis Testing: 6.1 One Sample (45 mins)

  • Read pages 6.1-1–6.1-14 and 6.1-31–6.1-34.
  • Understand inference, estimation, and the basics of hypothesis testing.

URL 6. Statistical Inference and Hypothesis Testing: 6.2 Two Samples (28 mins)

  • Read pages 6.2-1–6.2-7 and 6.2-11–6.2-14.
  • Understand inference, estimation, and the basics of hypothesis testing.

URL When to use the Bonferroni correction (26 mins)

  • Read the entire article.
  • Understand implications of multiple testing and Bonferroni correction.

URL P-Value, A True Test of Statistical Significance? A Cautionary Note (33 mins)

  • Read the entire article.
  • Understand and be able to correctly interpret p-values.

URL Statistical Notes for Clinical Researchers: Type I and Type II errors in statistical decision (16 mins)

  • Read the entire article.
  • Understand the importance and implications of Type I and Type II errors.

URL Common Pitfalls in Statistical Analysis: P-values, statistical significance, and confidence intervals (7 mins)

  • Read the entire article.

Module 4: Lesson 3: Power and Sample Size URL Statistical Reasoning II. Lecture Notes (80 mins)

  • Scroll down to the heading "Module 1: Issues in Study Design" and click on the PDF links to parts A through D of Lecture 3 (Power and Sample Size: Issues in Study Design). Read the slides. An audio recording of the presentation is also available.
  • Understand factors that affect study power and sample size requirements, and how they impact study design.

URL Sample Size Estimation in Clinical Trial (16 mins)

  • Read the entire article.
  • Understand factors that affect study power and sample size requirements, and how they impact study design.

URL Biostatistics Series Module 5: Determining Sample Size (43 mins)

  • Read the entire article.

URL How to Calculate Sample Size for Different Study Designs in Medical Research (35 mins)

  • Read the entire article.

URL Sample Size Calculations: Basic principles and common pitfalls (43 mins)

  • Read the entire article.

Module 4: Lesson 4: Nonparametric Tests URL Nonparametric Tests (60 mins)

  • Read the web page. Then, click on pages 2 through 8 and read the content of each page.
  • Summarize and describe nonparametric tests and understand the conditions under which they are applied.

URL Statistical Notes for Clinical Researchers: Nonparametric statistical methods: 1. Nonparametric methods for comparing two groups (11 mins)

  • Read the entire article.
  • Summarize and describe non-parametric tests and understand the conditions under which they are applied.

URL Statistical Notes for Clinical Researchers: Nonparametric statistical methods: 2. Nonparametric methods for comparing three or more groups and repeated measures (9 mins)

  • Read the entire article.
  • Summarize and describe nonparametric tests and understand the conditions under which they are applied.

URL Basic Biostatistics for Post-Graduate Students (18 mins)

  • Scroll down and read the section "Power of Study". This section outlines "How to Choose an Appropriate Statistical Test" as well as "Common problems faced by researchers in any trial and how to address them". The rest of this article is considered optional reading.

URL Choosing a Statistical Test (8 mins)

  • Read the web page.

Module 5: Lesson 1: Simple Linear Regression Analysis URL Introductory Statistics (55 mins)

  • Download the PDF version of the text by clicking on the appropriate link.  Then, in Chapter 12 titled "Linear Regression and Correlation", read the introductory section along with sections 12.1, 12.2, 12.3, and 12.6 (pages 679-691 and 697-704).
  • Understand linear relationships, outliers, and the basics of correlation

URL Correlation and linear regression (20 mins)

  • Read down to the beginning of the section titled "How the test works".
  • Understand the difference between correlation and simple linear regression, and when to apply one or the other

URL 1.1 - What is Simple Linear Regression? (40 mins)

  • Read the web page as well as sections 1.2 and 1.3. Access sections 1.2 (What is the "Best Fitting Line"?) and 1.3 (The Simple Linear Regression Model) by clicking on the titled links found on the left side of the web page.
  • Understand the difference between correlation and simple linear regression, and when to apply one or the other

URL Statistical Reasoning II:  Lecture Notes (40 mins)

  • Scroll down to the heading titled "Module 2: Linear Regression", and click on the PDF links to parts A to F of Lecture 4 (Simple Linear Regression).  Read the slides.  An audio recording of the presentation is also available.  
  • Understand the difference between correlation and simple linear regression, and when to apply one or the other

URL Lesson 2: SLR Model Evaluation (60 mins)

  • Read the web page as well as sections 2.1 to 2.5. Access sections 2.1 (Inference for the Population Intercept and Slope), 2.2 (Another Example of Slope Inference), 2.3 (Sums of Squares), 2.4 (Sums of Squares (continued)), and 2.5 (Analysis of Variance: The Basic Idea) by clicking on the titled links found on the left side of the web page.
  • Understand the difference between correlation and simple linear regression, and when to apply one or the other

URL Inferential Statistics for b and r (2 mins)

  • Scroll down and read the section titled "Assumptions" to understand the definition of Homoscedasticity.
  • Understand homoscedasticity, and its applications to correlation and regression

URL Lesson 3: SLR Estimation & Prediction (30 mins)

  • Read the web page as well as sections 3.1 to 3.3. Access sections 3.1 (The Research Questions), 3.2 (Confidence Interval for the Mean Response), and 3.3 (Prediction Interval for a New Response) by clicking on the titled links found on the left side of the web page.
  • Understand linear regression and how it relates to prediction

URL Lesson 4: SLR Model Assumptions (50 mins)

  • Read the web page as well as sections 4.1 to 4.8. Access sections 4.1 (Background), 4.2 (Residuals vs. Fits Plot), 4.3 (Residuals vs. Predictor Plot), 4.4 (Identifying Specific Problems Using Residual Plots), 4.5 (Residuals vs. Order Plot), 4.6 (Normal Probability Plot of Residuals), 4.7 (Assessing Linearity by Visual Inspection), and 4.8 (Further Examples) by clicking on the titled links found on the left side of the web page.
  • Understand linear regression and how it relates to prediction

URL Common pitfalls in statistical analysis: The use of correlation techniques (12 mins)

  • Read the entire article.

URL Introductory Statistics (40 mins)

  • Download the PDF version of the text by clicking on the appropriate link.  Then, read sections 12.4 and 12.5 titled "Testing the Significance of the Correlation Coefficient" and "Prediction" respectively (pages 691-697).

Module 5: Lesson 2: Multiple Linear Regression Analysis URL Statistical Reasoning II: Lecture Notes (60 mins)

  • Scroll down to the heading titled "Module 2: Linear Regression", and click on the PDF links to parts A to E of Lecture 5 (Relating a Continuous Outcome to More than One Predictor: Multiple Linear Regression).  Read the slides.  An audio recording of the presentation is also available.  
  • Understand multiple linear regression and its applications

URL Lesson 5: Multiple Linear Regression (30 mins)

  • Read the web page as well as sections 5.1 to 5.3. Access sections 5.1 (Example on IQ and Physical Characteristics), 5.2 (Example on Underground Air Quality), and 5.3 (The Multiple Linear Regression Model) by clicking on the titled links found on the left side of the web page.
  • Understand multiple linear regression and its applications

Module 5: Lesson 3: Logistic Regression Analysis URL Statistical Reasoning II: Lecture Notes (60 mins)

  • Scroll down to the heading titled "Module 3: Logistic Regression" , and click on the PDF links to parts A to E of Lecture 7 (Logistic Regression).  Read the slides.  An audio recording of the presentation is also available.
  • Understand simple logistic regression analysis

URL Logistic Regression (15 mins)

  • Scroll down to the heading titled "Task I: Describe Logistic Regression" and click on the link titled "Key Concepts About Logistic Regression". Then, read the content of that link.
  • Understand multiple logistic regression analysis and distinguish between adjusted and unadjusted regression coefficients

URL Statistical Reasoning II: Lecture Notes (60 mins)

  • Scroll down to the heading titled "Module 3: Logistic Regression" , and click on the PDF links to parts A to C of Lecture 8 (Multiple Logistic Regression).  Read the slides.  An audio recording of the presentation is also available. 
  • Understand multiple logistic regression analysis and distinguish between adjusted and unadjusted regression coefficients

URL 15.1 - Logistic Regression (60 mins)

  • Read the web page (section 15.1) as well as sections 15.2 to 15.3. Access sections 15.2 (Polytomous Regression) and 15.3 (Further Logistic Regression Examples) by clicking on the titled links found on the left side of the web page.
  • Understand multiple logistic regression analysis and distinguish between adjusted and unadjusted regression coefficients

URL Common pitfalls in statistical analysis: Odds versus risk (10 mins)

  • Read the entire article.
  • Be able (1) to distinguish between risks, absolute and relative risks, as well as odds and odds ratios, and (2) to differentiate relative risks from odds ratios and know how to conduct both methods

URL Common pitfalls in statistical analysis: Absolute risk reduction, relative risk reduction, and number needed to treat (10 mins)

  • Read the entire article.
  • Be able (1) to distinguish between risks, absolute and relative risks, as well as odds and odds ratios, and (2) to differentiate relative risks from odds ratios and know how to conduct both methods

URL 8.3 - Risk, Relative Risk and Odds (5 mins)

  • Read the entire web page.
  • Be able (1) to distinguish between risks, absolute and relative risks, as well as odds and odds ratios, and (2) to differentiate relative risks from odds ratios and know how to conduct both methods

URL Common pitfalls in statistical analysis: Logistic regression (17 mins)

  • Read the entire article.

URL 15.4 - Poisson Regression (30 mins)

  • Read the web page (section 15.4) as well as sections 15.5 to 15.8. Access sections 15.5 (Generalized Linear Models), 15.5 (Nonlinear Regression), 15.7 (Exponential Regression Example), and 15.8 (Population Growth Example) by clicking on the titled links found on the left side of the web page.

Module 5: Lesson 4: Overview of Correlation and Regression Analysis URL Principles of Regression Analysis (24 mins)

  • Read the entire article.
  • Be able (1) to distinguish between correlation, linear and multiple regression, as well as logistic regression, and (2) to understand the purpose and methods of linear (simple and multiple) and logistic regression including when to use each of them

URL Introduction to Multivariate Regression Analysis (31 mins)

  • Read the entire article.

Module 6: Lesson 1: Confounding and Effect Modification URL 3.5 - Bias, Confounding and Effect Modification (30 mins)

  • Read the web page.
  • Know the definition of a confounder, and understand the concepts of adjustment and stratification, as well the concepts of confounding and effect modification

URL Statistical Reasoning II: Lecture Notes (75 mins)

  • Scroll down to the heading titled "Module 1: Logistic Regression" , and click on the PDF links to parts A to C of Lecture 2 (Confounding and Effect Modification).  Read the slides.  An audio recording of the presentation is also available.
  • Identify the statistical techniques for dealing with confounding and effect modification, their strengths and limitations

URL Confounding and Effect Measure Modification - Conditions Necessary for Confounding (60 mins)

  • Read the web page. Then, click on pages 4-9 and read the content of each page.
  • Identify the statistical techniques for dealing with confounding and effect modification, their strengths and limitations

URL Multivariable Methods - Effect Modification (8 mins)

  • Read the entire web page.

URL 6.4 - Error, Confounding, Effect Modification in Ecological Studies (2 mins)

  • Read the entire web page.

URL Confounding Bias, Part I (60 mins)

  • Read the entire document.

URL Confounding Bias, Part II and Effect Measure Modification (60 mins)

  • Read the entire document.

Module 6: Lesson 2: Confounders and their Impact on Study Conclusions URL Cigarette smoking: an underused tool in high-performance endurance training (9 mins)
  • Read the entire article.
  • Be able to comment on the validity of study conclusions with respect to confounding and alternative explanations and appreciate that association neither means causation nor indicates the directionality of potential cause and effect
URL Assessing bias: the importance of considering confounding (15 mins)

  • Read the entire article.
  • Identify potential confounders in a relationship from a theoretical perspective and understand the consequences of using faulty reasoning and improper methods in studies

Module 7: Lesson 1: Limitations and Misinterpretations of Biostatistics URL Statistical methods used in the public health literature and implications for training of public health professionals (32 mins)

  • Read the entire article.
  • Understand and explain the relative strengths and limitations of biostatistics as it applies in various settings in public health

URL Helping Doctors and Patients Make Sense of Health Statistics (100 mins)

  • Scroll down and read sections I to III.  The rest of the article is optional reading.
  • Be able to detect misleading claims and inappropriate methods in research papers as well as appreciate that a statistically significant result may not be clinically significant

URL Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations (55 mins)

  • Read the entire article.
  • Be able to detect misleading claims and inappropriate methods in research papers as well as appreciate that a statistically significant result may not be clinically significant

URL The misuse and abuse of statistics in biomedical research (22 mins)

  • Read the entire article.
  • Appreciate that many interests may influence researchers towards favourable interpretation and presentation of their findings

Module 7: Lesson 2: Biostatistics in Public Health Programs URL National Health and Nutrition Examination Survey - Data Accomplishments (8 mins)

  • Read the entire web page.
  • Understand that public health programs rely on biostatistics principles and methodologies to collect, analyze, use, and present data

URL Did You Know? Video Series (5 mins)

  • Scroll down, and under the tab titled "Choose a video:", highlight the link titled "Cancer Statistics". Watch the entire video.
  • Understand that public health programs rely on biostatistics principles and methodologies to collect, analyze, use, and present data

URL Biostatistics: fundamental concepts and practical applications (6 mins)

  • Read page 17 of the article.
  • Understand that public health programs rely on biostatistics principles and methodologies to collect, analyze, use, and present data

URL CDC’s Alcohol Screening and Brief Intervention Efforts
  • Read the entire page.
URL Bringing Data Science to Addiction Research #3
  • Watch the video from 42:50 to 50:05.
URL Lessons from Prevention Research DrugFacts
  • Read the entire page.
URL Finding Evidence-based Programs and Practices
  • Explore the links under "Behavioral Health Resources" to become familiar with prevention programs and practices that have been developed based on evidence obtained from research and statistical data:
URL Biostatistics: fundamental concepts and practical applications (15 mins)

  • Read pages 18 to 21 of the article.

Course Activities Page Peer Activities