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Increasing variance in the sample tends to increase the sample size required to achieve a given power level. Conventional acceptable values for power and Î± are 80% or above and 5% or below, respectively, when calculating sample size. Smaller Î± or larger power will increase sample size. Power denotes the probability of detecting a difference when a difference does exist between the groups being compared. When considering the risk of Type 2 error, it is more intuitive to think in terms of power of the study or (1 âˆ’ Î²). This may be viewed as a false negative result. A Type 2 error occurs when one concludes that difference does not exist when, in reality, a difference does exist, and it is equal to or larger than the effect size defined by the alternative to the null hypothesis. A Type 1 error occurs when one concludes that a difference exists between the groups being compared when, in reality, it does not. Two types of false conclusion may occur, called Type 1 and Type 2 errors, whose probabilities are denoted by the symbols Ïƒ and Î². Any method for deriving a conclusion from experimental data carries with it some risk of drawing a false conclusion. The probability of Type 1 and Type 2 errors, the expected variance in the sample and the effect size are the essential determinants of sample size in interventional studies. An adequate sample ensures that the study will yield reliable information, regardless of whether the data ultimately suggests a clinically important difference between the interventions or elements being studied.

Biostatistics Series Module 5: Determining Sample Sizeĭetermining the appropriate sample size for a study, whatever be its type, is a fundamental aspect of biomedical research.

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