Understanding Statistics in Medical Literature - Online Course
A 4-Day Livestream Seminar Taught by
Lucy D’Agostino McGowanTuesday, July 22 –
Friday, July 25, 2025
10:30am-12:30pm (convert to your local time)
1:30pm-3:00pm
In today’s fast-paced healthcare landscape, understanding data and statistics is essential for making informed decisions. Whether you’re a medical student navigating your first journal article or a healthcare professional hoping to apply the latest research to patient care, the ability to critically evaluate medical literature is a vital skill.
This course is designed to introduce you to the core concepts of data and statistics, equipping you with the tools to extract meaningful insights from research without becoming bogged down in complex mathematical notation.
Starting July 22, we are offering this seminar as a 4-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
Closed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
More details about the course content
This course covers how to understand statistics in medical research papers without requiring advanced math skills. You’ll learn to evaluate different types of studies, read data visualizations, and interpret common statistical concepts like p-values and odds ratios.
Topics include study design (randomized trials, observational studies, and meta-analyses), data collection methods, how to read enrollment flowcharts and data tables, statistical testing, regression models, survival analysis, and diagnostic test metrics. You’ll learn how to assess whether research findings are trustworthy and can be reproduced. These skills will help you evaluate medical literature critically and apply research findings to clinical decisions.
This course covers how to understand statistics in medical research papers without requiring advanced math skills. You’ll learn to evaluate different types of studies, read data visualizations, and interpret common statistical concepts like p-values and odds ratios.
Topics include study design (randomized trials, observational studies, and meta-analyses), data collection methods, how to read enrollment flowcharts and data tables, statistical testing, regression models, survival analysis, and diagnostic test metrics. You’ll learn how to assess whether research findings are trustworthy and can be reproduced. These skills will help you evaluate medical literature critically and apply research findings to clinical decisions.
Computing
This course does not involve computing. Course exercises involve students extracting information from publicly available journal articles and answering conceptual questions about the content. As such, no particular computing packages are required.
This course does not involve computing. Course exercises involve students extracting information from publicly available journal articles and answering conceptual questions about the content. As such, no particular computing packages are required.
Who should register?
Tailored for busy students and professionals, this high-level introduction prioritizes efficiency and accessibility. By focusing on the big picture rather than statistical formulas, this course ensures that anyone—regardless of their prior knowledge—can develop a stronger understanding of statistical concepts common in medical literature.
Whether you’re looking to enhance your academic studies or improve patient outcomes through evidence-based practice, this course is the perfect starting point.
Tailored for busy students and professionals, this high-level introduction prioritizes efficiency and accessibility. By focusing on the big picture rather than statistical formulas, this course ensures that anyone—regardless of their prior knowledge—can develop a stronger understanding of statistical concepts common in medical literature.
Whether you’re looking to enhance your academic studies or improve patient outcomes through evidence-based practice, this course is the perfect starting point.
Seminar outline
Day 1: Data collection and study design
Overview of why statistics is important in medicine
-
- Fundamentally, it’s how we improve health & quality of life, reduce morbidity & mortality
Understanding data collection
-
- What are processing steps?
- What is the garden of forking paths?
- Understanding data sharing
Study design
-
- Randomized
- Observational
- Cohort studies
- Case-control studies
- Case series
- Meta-analyses
Day 2: Enrollment, Table 1, and types of statistical questions
Reading enrollment flowcharts
-
- Potential biases in sampling scheme
- Population versus sample
- Potential confounding biases
Reading Table 1
-
- Sample size
- Power
- Confounders
- Balance
Types of statistical questions
-
- Uncertainty
- Precision
- Accuracy
- Estimation
- Association versus causation
Day 3: Data visualizations and hypothesis testing
Understanding statistical graphics
-
- Histograms
- Scatterplots
- Box plots
- Dynamite plots
- Forest plots
- *Note: We will cover survival curves later.
Understanding hypothesis testing
-
- Association – what does it mean?
- Interpreting standard errors
- Interpreting confidence intervals
- What are p-values?
- Effect size and clinical/scientific vs. statistical significance
Day 4: Statistical models, diagnostic tests, and reproducibility
Understanding adjusted models
-
- Logistic versus linear regression
- Interpreting coefficients
- What is an odds ratio?
- Understanding a survival analysis
- Understanding a survival curve
- What is the x axis
- What is the y-axis
- What are the drops (events)?
- What are the ticks (censoring)?
- Understanding a hazard ratio
Understanding classification: diagnostic and screening tests
-
- What is sensitivity?
- What is specificity?
- What is the positive predictive value?
- What is the negative predictive value?
- Bias in predictive models (due to sampling bias, etc.)
- Brief introduction to machine learning
Reproducibility and replication
-
- Can I reproduce this result?
- Interpreting the study in the context of other evidence
- Has this been replicated elsewhere?
- Sharing data
- How can I access the data?
- How do I share my own data?
Related articles and examples can be found here.
Day 1: Data collection and study design
Overview of why statistics is important in medicine
-
- Fundamentally, it’s how we improve health & quality of life, reduce morbidity & mortality
Understanding data collection
-
- What are processing steps?
- What is the garden of forking paths?
- Understanding data sharing
Study design
-
- Randomized
- Observational
- Cohort studies
- Case-control studies
- Case series
- Meta-analyses
Day 2: Enrollment, Table 1, and types of statistical questions
Reading enrollment flowcharts
-
- Potential biases in sampling scheme
- Population versus sample
- Potential confounding biases
Reading Table 1
-
- Sample size
- Power
- Confounders
- Balance
Types of statistical questions
-
- Uncertainty
- Precision
- Accuracy
- Estimation
- Association versus causation
- Uncertainty
Day 3: Data visualizations and hypothesis testing
Understanding statistical graphics
-
- Histograms
- Scatterplots
- Box plots
- Dynamite plots
- Forest plots
- *Note: We will cover survival curves later.
Understanding hypothesis testing
-
- Association – what does it mean?
- Interpreting standard errors
- Interpreting confidence intervals
- What are p-values?
- Effect size and clinical/scientific vs. statistical significance
Day 4: Statistical models, diagnostic tests, and reproducibility
Understanding adjusted models
-
- Logistic versus linear regression
- Interpreting coefficients
- What is an odds ratio?
- Understanding a survival analysis
- Understanding a survival curve
- What is the x axis
- What is the y-axis
- What are the drops (events)?
- What are the ticks (censoring)?
- Understanding a hazard ratio
- Understanding a survival curve
Understanding classification: diagnostic and screening tests
-
- What is sensitivity?
- What is specificity?
- What is the positive predictive value?
- What is the negative predictive value?
- Bias in predictive models (due to sampling bias, etc.)
- Brief introduction to machine learning
Reproducibility and replication
-
- Can I reproduce this result?
- Interpreting the study in the context of other evidence
- Has this been replicated elsewhere?
- Sharing data
- How can I access the data?
- How do I share my own data?
Related articles and examples can be found here.
Payment information
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.