New Stats4PT Lesson: Bayesian Reasoning for Clinical Decision-Making
Stats4PT Lesson Three
Bayesian reasoning isn’t just a statistical tool—it’s a framework that mirrors how clinicians naturally refine their understanding in response to new evidence. Instead of rigidly categorizing findings as “significant” or “not significant,” Bayesian inference allows us to ask: How likely is this to be true, given what we already know?
In the latest Stats4PT lesson, we explore:
Why Bayesian reasoning aligns with clinical decision-making.
How Bayes’ Theorem inverts conditional probabilities to refine diagnoses and treatment choices.
The limitations of frequentist statistics in practice and why Bayesian methods provide a more intuitive approach.
How Bayesian reasoning integrates with causal inference and Bhaskar’s domains of reality.
Check out the full lesson here: Bayesian Reasoning for Clinical Decision-Making
This is the first in a series of three lessons on Bayesian reasoning. In a couple of weeks, we’ll dive into Bayesian Applications in Research and Evidence Synthesis, covering how Bayesian methods enhance clinical trials, meta-analyses, and systematic reviews—particularly in causal-critical-realist reviews (CCRRs).
If you’re ready to rethink how statistical inference connects to clinical reasoning, this lesson is for you!
Let me know your thoughts, and feel free to share with colleagues interested in refining their approach to evidence-based practice.