If you’re trained in epidemiology or biostatistics, you likely think in terms of models, inference, and evidence. Now, with machine learning entering the scene, you’re probably hearing about algorithms that can “predict” disease, “detect” outbreaks, and “learn” from data. But while ML offers exciting possibilities, it’s important to understand how it differs from classical statistical…
All posts in Statistics
Training Critical Thinking and Logical Thinking in the Age of AI for Biostatistics and Epidemiology
The arrival of generative AI tools like ChatGPT is changing the way we teach and practise biostatistics and epidemiology. Tasks that once took hours, like coding analyses or searching for information, can now be completed within minutes by simply asking the right questions. This development brings many opportunities, but it also brings new challenges. One…
Understanding the Central Limit Theorem and Estimating Population Mean Using Sample Data
The Central Limit Theorem (CLT) is a fundamental concept in statistics and an essential tool in biostatistics. It provides a foundation for understanding how sample data can be used to make inferences about an entire population. This article will guide students through the development and significance of the CLT, exploring the role of sample means,…
All Swans Are White
Photo by John Harrison Imagine that every swan observed in a particular region is white, leading to the belief that “all swans are white.” This conclusion appears reliable until the unexpected discovery of a black swan, which disproves this assumption. This example demonstrates a fundamental principle of hypothesis testing: science is often less about proving…