Statistical intervals are a vital tool for understanding the uncertainty for an estimate.
Confidence intervals are commonly used with sample size determination methods based on a target level of precision for many scenarios. However, sample size methods also exist for other intervals such as prediction and tolerance intervals as well as Bayesian approaches such as credible intervals.
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Statistical intervals are an essential tool for understanding the uncertainty in our estimates from statistical tests and models. Sample size determination methods can help a trialist understand how many subjects are needed to achieve a required level of precision in the intervals of most interest.
Confidence intervals are the most widely used type of statistical interval which gives the expected uncertainty for a parameter under repeated sampling. Sample size methods for confidence intervals are developed for a wide variety of endpoints, regression models and study designs. These sample size methods typically target a specific level of precision in the estimate.
However, sample size determination also exists for other less common interval types. Examples include Prediction Intervals, Tolerance Intervals and Bayesian Credible Intervals. We explore each of these intervals, how they compare to confidence intervals and what is the target of interest in their respective sample size methods.
In this webinar, we introduce the variety of statistical intervals available and describe methods to calculate the required sample size for confidence, prediction, tolerance and credible intervals.