![]() ![]() Consequently, there is a strong ethical justification for researchers to ensure that the data they collect are sufficient and of adequate quality such as to maximize the likelihood of the research contributing to practically useful conclusions. Patients in clinical trials may be subjected to the risks of receiving potentially useless or harmful new treatments, or of not receiving a beneficial new treatment if they are assigned to a control arm. Research has significant costs in terms of organizational outlay and staffing, and also the potential costs to patients and subjects. 1 The purpose of this article is to outline the issues involved and to describe the rationale behind sample size and power calculations. Sample size and power considerations should therefore be part of the routine planning and interpretation of all clinical research. Larger sample sizes should lead to more reliable conclusions. Medical research sets out to form conclusions applicable to populations with data obtained from randomized samples drawn from those populations. ![]() Type II errors occur when the null hypothesis is incorrectly accepted, meaning that research fails to identify a significant difference or effect that actually exists. ![]() Type I errors occur when the null hypothesis is incorrectly rejected, meaning that research incorrectly identifies a difference or effect as significant where no such difference exists. Post hoc power estimations can help identify important ‘negative’ studies where no significant difference or effect is found. Sample size calculations require the minimum standardized difference, type I error rate, and power. The reporting of the sample size calculation is useful to identify the main outcome and the minimum difference or effect of clinical importance. ![]()
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