Free learning series

stats4PT

The language of scientific inquiry for physical therapists, educators, students, and researchers.

Evidence is more than a number

Stats4PT examines how observations become evidence, estimates, and warranted scientific claims through statistical inference, Bayesian reasoning, causal reasoning, and critical reflection.

The course

Build from inference to causal reasoning

  1. 01Introduction to Statistical Inference & InductionHow we draw conclusions from data and connect structured evidence to clinical learning.
  2. 02Understanding Causality in Clinical ResearchMove beyond association to mechanisms, contexts, and causal models.
  3. 03Bayesian Reasoning for Clinical Decision-MakingUse evolving evidence to update beliefs under uncertainty.
  4. 04Bayesian Applications in Research and Evidence SynthesisExplore adaptive trials, meta-analysis, and critical realist reviews.
  5. 05Bayes’ Theorem in Clinical Decision-MakingApply prior, likelihood, and posterior probability to diagnosis and prognosis.
  6. 06Beyond Data: Mechanisms and Structures in EvidenceKnow when a population model applies and when a mechanism matters.
  7. 07Building a Causal Model for Clinical ResearchMake assumptions visible with directed acyclic graphs and worked examples.
  8. 08Statistical Fallacies and Biases in Clinical ResearchRecognize causal illusions in design, interpretation, and personal experience.

Clinical inquiry ecosystem

Distinct forms of scholarship, connected

Stats4PT does not connect every project as one educational path. It develops discovery-oriented inquiry that contributes to, but remains distinct from, integrative model building and practice reasoning.

Discovery

stats4PT

Moves from observations toward scientific claims through the language and methods of statistical and causal inquiry.

Generative mechanisms

Physiolog

Contributes physiological knowledge about how and why effects occur, adding causal depth beyond interventions and outcomes.

Integrative scholarship

Models4PT

Integrates evidence, mechanisms, context, and uncertainty into comprehensive population-level causal models.

Practice scholarship

Clinical Inference Engine

Uses population knowledge with individual information to support inspectable, patient-specific practice reasoning.

stats4PT inquiry + Physiolog mechanisms → Models4PT integration → Clinical Inference Engine practice reasoning

About the author

Sean M. Collins, PT, ScD

Physical therapist and Professor of Clinical Inquiry in the Doctor of Physical Therapy Program at Plymouth State University.

Sean brings more than 30 years of experience in physical therapy education and more than 25 years of clinical and quantitative research to an open program of scholarship at the intersection of physiology, evidence, causal knowledge, and clinical reasoning.