Welcome to Stats4PT!
Bridging Statistics & Clinical Reasoning!
Read below for what this is all about - I’ve copied the planned topics here to serve as an easy to find linked contents of the series:
Topics Include:
Foundations of Clinical Reasoning and Statistics
Bayesian Reasoning as a Core Framework
Lesson 3: Bayesian Reasoning for Clinical Decision-Making – Why Bayesian thinking aligns with clinical reasoning, how it contrasts with frequentist thinking, and why it’s foundational for real-world inference.
Lesson 4: Bayesian Applications in Research and Evidence Synthesis – How Bayesian methods improve clinical trials, meta-analyses, and systematic reviews, particularly in causal-critical-realist reviews (CCRRs).
Lesson 5: Bayes’ Theorem in Clinical Decision-Making – Applying Bayesian inference to diagnostics, treatment selection, and prognosis.
Causal Modeling and Statistical Fallacies
Lesson 6: Beyond Data: Mechanisms and Structures in Evidence – Why mechanistic reasoning matters, integrating causal inference into clinical decision-making.
Lesson 7: Practical Applications: Building a Causal Model for Clinical Research – How to construct and apply causal models in research and clinical practice.
Lesson 8: Statistical Fallacies and Biases in Clinical Research – Common errors in reasoning, misinterpretations of statistics, and how to avoid them.
Integrating Critical Realist Reviews & Clinical Education
Lesson 9: Realist Reviews: Blending Statistics with Theoretical Reasoning – How critical realism enhances evidence synthesis, moving beyond frequentist meta-analyses.
Lesson 10: Teaching Statistical Reasoning in DPT Programs – How Bayesian methods, causal reasoning, and statistical inference should be taught in clinical education.
Conclusion: Inference as a Lifelong Clinical Inquiry Skill – Final reflections, practical takeaways, and the role of continuous learning.
Unlocking Statistical Insight for Physical Therapy Practice Through a Critical Realist Lens
Stats4PT is a space where statistical reasoning meets clinical inquiry to empower physical therapists, educators, and students with a deeper understanding of evidence, causality, and decision-making in healthcare.
This isn’t just about p-values and confidence intervals — it’s about understanding why and how evidence (but not just evidence) informs clinical reasoning in real-world patient care.
The philosophy of Stats4PT is that statistics are simply tools that allow us to summarize our experiences. Communicating our experiences is vital for our profession, our professional growth, and therefore the health and well being of people seeking our care. Understanding statistics and the language it uses to communicate experiences is essential for knowing when to, or not to, accept the experience being communicated.
Stats4PT considers every use of statistics an attempt to share experiences, experiences that include any number of observations. Let’s face it, once you get past 2-3 observations it becomes easier to somehow summarize them. That’s what statistics does. The challenge is then taking these experiences and thinking about what they mean for your decision making when working with the ONE PERSON in front of you during a clinical encounter.
What is Stats4PT All About?
Stats4PT is more than a statistics course. It’s a guided exploration of how statistical tools, causal reasoning, and critical thinking can elevate clinical practice and research in physical therapy.
We’ll connect:
Statistical principles (e.g., inference, induction, Bayesian reasoning).
Causal reasoning frameworks, grounded in Critical Realism.
Practical tools for data analysis using R (and Jamovi).
Clinical application, bridging numbers and real-world patient care.
This resource draws on foundational resources such as:
Introductory Statistics (Open Stax book)
A Realist Theory of Science by Roy Bhaskar
Rethinking Causality, Complexity, and Evidence for the Unique Patient Editors: Rani Lill Anjum, Samantha Copeland, Elena Rocca
Personal Knowledge by Michael Polanyi
Several of my own writings and lecture notes on causal inference, statistical inference (induction), Bayesian inference (abduction) and graphical models
Series Goals:
Bridge the gap between statistical methods and clinical reasoning in physical therapy.
Align statistical discussions with the philosophy of critical realism (Bhaskar) and the Clinical Inquiry Fellowship's mission.
Empower clinicians to interpret statistical evidence meaningfully across empirical, actual, and real domains.
Provide practical workflows in R and Jamovi for causal modeling and statistical inference.
Foster critical engagement with traditional evidence-based frameworks.
How to Use Stats4PT
Main Hub: This page serves as your anchor. Bookmark it and return often!
Core Topics: Structured Pages will dive into core statistical and causal reasoning themes.
Think of these as your textbook chapters, always accessible and neatly organized.
Announcement Posts: Every time a new Page is added, you’ll get a notification post with a direct link (if you subscribe)
Interactive Content:
Look out for:
Videos explaining key concepts.
R and Jamovi workflows for hands-on practice.
Case studies demonstrating real-world application.
Community Engagement: Share your thoughts, ask questions, and suggest topics in the comments! Clinical inquiry fellows and PSU DPT students will be following along as well, and they will be working with me directly :)
Who is Stats4PT For?
DPT Students: Build confidence in interpreting research and applying statistics.
Clinicians: Strengthen your evidence-based practice with better reasoning tools.
Educators: Access resources for teaching statistical reasoning in physical therapy programs.
Researchers: Explore causal modeling and statistical inference frameworks.
Clinical Inquiry Fellows: Deepen your integration of critical realism and statistical reasoning into clinical practice, mentorship, and scholarly contributions.
PhD/ScD/EdD Doctoral Students: Seeking mentoring or consulting support for coursework, research projects, or dissertation development? I’m available as a consultant to help you navigate statistical reasoning, causal modeling, and critical realism in your academic work.
If you're curious about:
Why statistical results are not the same as causal claims,
How ontology shapes our interpretation of evidence,
Why patient experiences matter as much as p-values,
How to mentor others in applying these principles,
…you’re in the right place.
What to Expect
I’m committing to releasing at least one new topic (page) per month. The content will be here, so you can take it in at your pace.
Each topic will blend theoretical insights, practical workflows, and clinical applications. Some of these topics may end up being more than one page of content.
Stay Updated!
Subscribe to receive updates on new pages and posts.
Engage in Comments: Share your thoughts and questions (see below).
Bookmark this Page as your main reference point for Stats4PT.
Why Stats4PT Matters
Statistics isn't just about crunching numbers — it’s about making sense of uncertainty in a way that improves patient care by understanding how people (including you) share their experiences.
Whether you're analyzing research, teaching future clinicians, or making day-to-day clinical decisions, Stats4PT will sharpen your reasoning toolkit.
Together, we’ll explore:
What the numbers mean.
What they don’t mean.
And how they fit into the larger reality of clinical care.