45. How to Interpret Statistical Findings in Research

About this lesson

Welcome to Module 3.15, where we focus not just on statistical calculations, but on what the results mean. In healthcare research, interpreting statistics correctly is just as important as computing them. This 3-minute module explores how to distinguish between statistical and practical significance, interpret effect sizes, and avoid common misinterpretations that can lead to flawed conclusions.

What You'll Learn

• The key difference between statistical significance and clinical importance
• How effect sizes like Cohen’s d, odds ratios, risk ratios, and NNT help determine the real-world impact
• Why p-values alone aren't enough and how confidence intervals add crucial context
• How sample size and multiple testing influence statistical significance
• Common errors in statistical interpretation, including correlation vs. causation

This module is essential for researchers, clinicians, and students who want to move beyond numbers and draw meaningful, applicable insights from research findings.

Topics Covered

• Practical vs. Statistical Significance
• Interpreting Confidence Intervals
• Measures of Effect Size in Health Research
• Avoiding Common Statistical Misconceptions
• Applying Evidence in Clinical Contexts

Who Should Watch This

• Healthcare professionals and clinical decision-makers
• Public health and policy researchers
• Students in research methods, biostatistics, or epidemiology
• Anyone evaluating or applying statistical results in practice

Learn to critically evaluate data and translate findings into evidence-based healthcare decisions.

Lesson notes

Full written notes for this lesson are in production. Until they publish, watch the video above and use the summary on this page as your study guide.