Anesthesia and analgesia
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Anesthesia and analgesia · Feb 2018
Significance, Errors, Power, and Sample Size: The Blocking and Tackling of Statistics.
Inferential statistics relies heavily on the central limit theorem and the related law of large numbers. According to the central limit theorem, regardless of the distribution of the source population, a sample estimate of that population will have a normal distribution, but only if the sample is large enough. The related law of large numbers holds that the central limit theorem is valid as random samples become large enough, usually defined as an n ≥ 30. ⋯ Sample size for a given relative reduction in proportions depends heavily on the proportion in the control group itself, and increases as the proportion decreases. Sample size for single-group studies estimating an unknown parameter is based on the desired precision of the estimate. Interim analyses assessing for efficacy and/or futility are great tools to save time and money, as well as allow science to progress faster, but are only 1 component considered when a decision to stop or continue a trial is made.