BioStatistics

Imagine having the tools to analyze health data, design effective studies, and make evidencebased decisions that improve patient outcomes. This course in biostatistics provides you with a comprehensive foundation in the statistical methods that drive research in medicine and public health. From understanding data types to mastering complex tests, you'll gain hands-on experience with the techniques that make data meaningful and actionable. Through practical applications and real-world examples, you'll explore topics like probability theory, hypothesis testing, regression analysis, ANOVA and.

Biomedical Foundations

14 chapters. Online and self-paced, in English, on the BMEssentia Platform.

Open in the Platform Start the free trial

What you will learn

Learn to classify and work with different data types (nominal, ordinal, interval, ratio) and their implications for statistical analysis
Master techniques for summarizing data, including measures of central tendency and variability, to provide clear and meaningful insights.
Develop a strong grasp of probability, including conditional probabilities and common distributions like binomial, Poisson, and normal distributions.
Learn to test hypotheses with confidence, using t-tests, chi-square tests, and more to make informed conclusions about population data.
Conduct analyses of variance (ANOVA) to compare means across groups, interpreting results for applications like clinical trials and epidemiology studies.
Build predictive models with linear and logistic regression, exploring relationships between variables and making data-driven predictions.
: Use nonparametric tests, such as the MannWhitney U test, to analyze data that doesn't follow a normal distribution, ensuring accurate results.
Gain skills in selecting appropriate sampling methods and calculating sample sizes to ensure study validity and statistical power.
Learn to conduct power analyses, optimizing study designs to detect meaningful effects and improve the reliability of results.
Develop proficiency in interpreting statistical outputs from software like SPSS, R, or SAS, applying results directly to research and health studies.

Modules

Introduction to Biostatistics
Descriptive Statistics
Probability Theory
Sampling Distributions and the Central Limit Theorem
ANOVA (Analysis of Variance)
The Special Case of Two Groups: The t-Test
Contingency Tables, Chi-Square Test, z-Test
Fisher Exact Test, Relative Risk, Odds Ratio
Power and Sample Size
Paired t-Test, Repeated Measures of Analysis of Variance, McNemar's Test
Nonparametric Tests: Mann-Whitney Rank-Sum Test, Wilcoxon Signed-Rank Test
Nonparametric Tests: Kruskal-Wallis Test, Friedman Test
Confidence Intervals
Correlation and Regression

More courses

All 22 courses

Learn it properly, inside the Platform

22 courses, the 69-device library with 13 sections per device, BMEssentials and BMEssence guides and MedTech AI. Start with a 7-day free trial, no card.

Start the 7-day free trial