Statistics for the Health Professions

MGH Institute of Health Professions via edX Microbachelors

Overview

This program introduces you to the foundations of statistical concepts as they apply to healthcare. Health professionals rely on statistics for a wide range of functions: from describing clinical populations, to evaluating pharmaceutical outcomes, and even increasing efficiency of clinics and hospitals. You’ll learn the statistical information and testing that is increasingly necessary and occurring in the health fields.

Across three carefully designed courses—Introduction to Statistical Concepts and Describing Data, Correlations and t-tests, and Repeated Measures ANOVA and Non-parametric Statistics—you will explore engaging lectures, practice problems, and stimulating assessments that mirror the expectations of credit-bearing coursework. Each course integrates practical application of statistics in healthcare, aiming to demonstrate the relevance in health and make clear links to professional fields.

The program balances a flexible approach with a rigorous academic experience. It is ideal for anyone looking to solidify their mathematical foundation before committing to the demands of a for-credit statistics course. By the end of the program, you will have a strong grasp of statistical theory as well as the academic habits needed for future success. You will gain confidence in the vocabulary, concepts, and data analysis common to introductory statistics courses.

Syllabus

Courses under this program:
Course 1: Introduction to Statistical Concepts and Describing Data

This course introduces key statistical concepts and data analysis techniques used in healthcare. Students will learn to classify variables, summarize data, interpret measures of central tendency and variability, and perform basic hypothesis testing. Emphasis is placed on applying descriptive statistics using R to analyze and interpret real-world healthcare data.

Course 2: Correlations and t-tests

This course builds on foundational concepts from Introduction to Statistical Concepts and Describing Data and focuses on examining relationships and comparing group means in healthcare data. Students will learn to calculate and interpret correlation coefficients, conduct independent sample t-tests and ANOVA, and evaluate the assumptions underlying these techniques. Practice examples using healthcare data will help reinforce the relevance of these tests for everyday decision-making in clinical and research environments.

Course 3: Repeated Measures ANOVA and Non-parametric Statistics

This course builds on the concepts from Introduction to Statistical Concepts and Describing Data and Correlation and t-tests and focuses on methodology used when analyzing data collected from the same subjects over time or under multiple conditions. Topics include paired sample t-tests, repeated measures ANOVA, the logic of within-subjects designs, and when to use non-parametric alternatives like the Wilcoxon signed-rank test or the Kruskal-Wallis test. Emphasis is placed on understanding when and why to apply these techniques, particularly in longitudinal studies and small-sample clinical research. By the end of the course, you’ll be equipped to analyze complex data structures and make informed decisions based on statistical output.

Courses

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4 weeks, 3-6 hours a week, 3-6 hours a week
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Introduction to Statistical Concepts and Describing Data is designed to give you a strong foundation in statistics with a focus on real-world healthcare applications. Whether you're pursuing a career in nursing, public health, health informatics, or clinical research, this course will help you build the skills and confidence to understand and apply data in meaningful ways.

You’ll begin by exploring essential statistical terms and concepts, including the difference between populations and samples, and the distinction between descriptive and inferential statistics. From there, you’ll learn how to classify variables, recognize common scales of measurement, and understand how data types influence the way results are analyzed and interpreted in healthcare.

A major emphasis of the course is learning how to describe and summarize data—using measures such as mean, median, mode, range, and standard deviation—to identify trends and make informed judgments. You’ll also be introduced to the logic of hypothesis testing and walk through the steps used to evaluate claims and draw evidence-based conclusions from sample data.

Throughout the course, you will engage in hands-on practice using R, a powerful statistical programming language widely used in healthcare and research. By analyzing real healthcare datasets, you’ll gain experience generating summaries, visualizing distributions, and interpreting results.
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4 weeks, 3-6 hours a week, 3-6 hours a week
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Correlations and t-tests builds on the foundational statistical skills you learned in Introduction to Statistical Concepts and Describing Data to help you explore relationships and compare group outcomes in healthcare data. In this course, you’ll start by learning how to measure the strength and direction of relationships between variables using correlation techniques. You will then learn how to use independent sample t-tests and ANOVA to quantify differences between groups.

With real-world healthcare examples and hands-on practice in R, you’ll gain practical experience analyzing data and interpreting results to inform evidence-based decisions. Topics include correlation interpretation, assumptions of t-tests and ANOVA, statistical significance, and the appropriate use of each method in clinical and research settings.

This course is ideal for learners pursuing careers in nursing, public health, clinical research, or allied health who want to strengthen their ability to analyze and interpret healthcare data with confidence.
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4 weeks, 3-6 hours a week, 3-6 hours a week
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Repeated Measures ANOVA and Non-parametric Statistics is the final course in the Statistics for the Health Professions MicroBachelors series, designed to help learners handle more complex and practical data analysis challenges often found in real healthcare settings. Building on earlier coursework in Correlations and t-tests , this course focuses on comparing outcomes across multiple time points or conditions and introduces techniques for analyzing non-normally distributed or ordinal data.

You will begin by exploring paired sample t-tests and repeated measures ANOVA, statistical methods used to analyze data collected from the same individuals across different times or treatments. This technique is essential for evaluating changes over time such as patient recovery, symptom severity, or clinical progress. You will learn how to conduct repeated measures analysis in R, assess within-subject variability, and interpret key outputs such as F-values and p-values.

Next, the course introduces non-parametric tests like the Wilcoxon signed-rank test, Mann-Whitney U test, and Kruskal-Wallis test. These tools are particularly useful when data violate the assumptions of normality or involve ranked/ordinal measures, which are common in survey responses, symptom scales, and clinical ratings.

This course emphasizes application in healthcare, providing learners with hands-on experience using R and real datasets to make meaningful, data-informed decisions in professional practice or research.