
Biostatistics blends biology and statistics to use data for health decisions, testing medicines in clinical trials, preventing outbreaks, and proving what actually works.
Learn to select representative samples using random, stratified, systematic, and cluster methods, minimize sampling bias, and ensure reliable, valid biostatistics conclusions.
Explore descriptive statistics in biostatistics by calculating and interpreting central tendency (mean, median, mode), dispersion (range, variance, standard deviation), and visualizing data with histograms and box plots, including outliers.
Explore probability as the foundation of biostatistical inference, measuring uncertainty and predicting outcomes, and learn independence, conditional probability, normal, binomial, Poisson, exponential distributions, and the central limit theorem.
Explore the normal (bell curve), binomial, Poisson, and exponential distributions in biostatistics, and learn to identify and apply them to real-world medical data.
Explore the t test, chi squared test, and Anova to compare means and analyze categorical data across one way Anova and two way Anova designs, with post-hoc options.
Learn nonparametric tests as alternatives to t tests and ANOVA for non-normal data, outliers, and small samples, including Mann-Whitney U test, Kruskal-Wallis test, and Wilcoxon signed rank test.
Learn how Bayesian inference updates probabilities with new data by combining prior knowledge, likelihood, and posterior to inform medical decision making, adaptive trials, and disease modeling in biostatistics.
Learn to perform survival analysis in RStudio, handle censoring, and interpret Kaplan-Meier curves to compare time-to-event between treatment groups.
What You'll Learn How To
Understand important biostatistical concepts including variables, distributions, and sampling
Use descriptive statistics and learn how to visualize and interpret data using histograms, boxplots, and scatterplots
Leverage inferential tests: t-tests, chi-square, ANOVA, correlation, and regression
Interpret results so that you can communicate statistical findings clearly and accurately
Course Description
Are you intimidated by statistics? Or maybe you're starting a health science, psychology, or biology program and need to learn biostatistics fast, without getting too overwhelmed?
This beginner-to-advanced course breaks down biostatistics into clear, step-by-step lessons using real-world examples and data. Whether you're a student, a researcher, educator, or health professional, you'll gain the skills you need to analyze and interpret data with confidence.
We start from the very basics, that is why no prior knowledge is required, and then build progressively to cover both descriptive and inferential statistics, while grounded in real-world healthcare and behavioral science examples. You'll also get hands-on demos with modern statistical software, helping you build analysis skills that employers and graduate schools value.
By the end, you’ll be equipped not only to run statistical tests—but also to explain and apply them in meaningful ways.
Who This Course is For
Students in nursing, psychology, biology, public health, or medicine
Researchers and professionals in healthcare, epidemiology, or social science
Educators teaching quantitative or research methods
Anyone preparing for grad school or research-based careers
Beginners who want a gentle, practical, and thorough introduction to biostatistics