Principle 1: P-values can indicate how incompatible the data are with a specified statistical model.Ī P-value is one of the ways of summarizing the incompatibility between the observed data and a proposed model for the data. The number of patients vomiting on the first postoperative day was lower in the treatment group (45/100) compared to the placebo group (60/100) with a P-value of 0.03. A new antiemetic Nopov has been tested against placebo in a sample of patients undergoing day care gynecological surgery. Let me postulate a specific clinical research scenario for this purpose. This editorial attempts to explain the salient features of this statement from the perspective of Indian anesthesiology research, based on the explanations provided by Wasserstein and Lazar. ![]() ![]() “If all else fails, use ‘significant at P > 0.05 level’ and hope no one notices.” (, Randall Munroe, Creative Commons Attribution-NonCommercial 2.5 License)Īs practicing physician-scientists, it is important for us to understand the context and “significance” of this statement.
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