Research Methods, Statistics, and Evidence-Based Practice
Core concepts to master
Candidates must read research critically: study design (cross-sectional, longitudinal, case-control, RCT), threats to validity, sampling, and measurement. Know core statistics - central tendency and dispersion, correlation vs. causation, effect size, confidence intervals, power, and the meaning of p-values. Understand diagnostic statistics: sensitivity, specificity, positive/negative predictive value, and how base rates change interpretation.
Evidence-based practice integrates best research evidence, clinical expertise, and patient values. Know levels of evidence and the difference between efficacy (controlled conditions) and effectiveness (real-world). Recognize common biases - selection, attrition, publication, and confirmation bias.
Traps
Equating statistical significance with clinical importance. Confusing sensitivity with predictive value. Ignoring base rates when interpreting a positive test. Treating correlation as causation. Overgeneralizing from small or unrepresentative samples.
Rule of thumb
When a test is positive, ask what is the pretest probability? Predictive value depends on prevalence; a highly sensitive test in a low-prevalence population yields many false positives. Always weigh effect size and confidence intervals alongside p-values.
Worked example: A screening test with 90% sensitivity and 90% specificity in a population with 1% prevalence yields a positive predictive value near 8% - most positives are false.