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DATA POINT - TESTS OF CLINICAL SIGNIFICANCE A HICS INITIATIVE

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  Dr Manoj Raju Prabandhakam Aster Narayanadri Hospital, Tirupati             Appropriate interpretation of research results from the clinical significance point is crucial for clinical decision-making and Evidence-based practice.   Clinical significance measures the practical, real-world impact of a treatment, distinct from statistical significance, which only indicates that findings are likely not due to chance (p< 0.05). From a clinical point of view, the statistically significant difference among groups is not of prime importance. (1) Before diving into clinical significance, the six points about p -value to consider are: (2) 1.       P  values can be indicative of the level of discrepancy between the data and a specific statistical model. 2.     P  values do not quantify the probability of the tested hypothesis being true or the probability of the data resulting solely from random chance. ...
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DATA POINT - A HICS INITIATIVE TESTS OF STATISTICAL SIGNIFICANCE

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  Decoding Statistical Significance 1. Introduction: Distinguishing a True Signal from Random Noise Statistical significance is an essential tool for the modern clinician. Its core purpose is to help determine if an observed effect in a medical study—such as the apparent benefit of a new drug—is a genuine finding or simply the result of random chance within the specific sample of patients studied. It serves as a preliminary filter, helping to distinguish a true signal from random noise. While this concept is a foundation of evidence-based medicine, a superficial understanding can be misleading. To correctly interpret the medical literature and apply it to patient care, it is crucial to understand the nuances, limitations, and key related concepts that provide a much fuller picture of a study's findings. Mastering the interpretation of medical evidence begins with a firm grasp of the core concepts that underpin all statistical testing. 2. The Core Toolkit: From P-Values to Confidenc...
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DATA POINT - A HICS INITIATIVE

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  Patterns of Data Dispersion in Medical Statistics 1. Why Dispersion Matters in Medicine Dispersion describes how spread out data values are around a central point. In clinical research, dispersion helps identify: Biological variability Measurement precision Treatment response differences Subgroups within a population Outliers that may signal pathology or error 2. Symmetry and Skewness in Dispersion A. Symmetric Dispersion Data spread is equal on both sides of the mean Mean ≈ Median ≈ Mode Often seen in controlled physiological variables Example: Adult male height B. Asymmetric (Skewed) Dispersion Right (Positive) Skew Long tail to the right Mean > Median Examples: Length of hospital stay, triglycerides Left (Negative) Skew Long tail to the left Mean < Median Examples: Age at onset of certain genetic disorders   ...
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