Confidence intervals

In this talk, the focus is confidence intervals, supported by coverage of the definition and interpretation of confidence intervals, highlighting their importance in conveying both estimate precision and uncertainty. We will discuss how confidence intervals differ from P values by offering direct insights into effect, size, direction, and the reliability of research findings. The lecture will underscore the value of confidence intervals in informed clinical decision making and ethical evidence communication. Finally, we'll emphasize best practices for robust statistical analysis in biomedical research to ensure credibility and minimize bias. Confidence intervals are a foundational concept in statistical analysis for interpreting data and making informed decisions, especially in health and clinical research. A confidence interval is a range derived from sample data that is likely to contain the true population parameter, such as a mean or proportion with a specified confidence level, often 95%. This range accounts for sampling variability. Importantly, the confidence level refers to the procedure, not to the probability for any one calculated interval. Let's explore interpreting confidence intervals. Unlike a single point estimate, a confidence interval reflects both our best estimate and the range of plausible values given chance variation. For instance, a 95% confidence interval of 1.2 to 3.8 percentage points indicates estimate precision.