--- title: Sample Size and Power for Non-Parametric Analysis description: "Free webinar: Learn about non-parametric analysis methods, tools, and sample size considerations in this free webinar. Explore the basics of non-parametric tests, including Mann-Whitney U and Wilcoxon signed-rank tests, and understand their application in clinical trials." image: https://www.statsols.com/hubfs/Sample%20Size%20and%20Power%20for%20Non-Parametric%20Analysis%20Sign%20Up.png --- [![nQuery Logo-White](https://www.statsols.com/hs-fs/hubfs/nQuery%20Logo-White.png?width=705&height=175&name=nQuery%20Logo-White.png "nQuery Logo-White")](https://www.statsols.com/) ** ** ![Sample size and power training for use in clinical trials](https://www.statsols.com/hs-fs/hubfs/Master_Images/___PageImages/Software_Training/Sample%20size%20and%20power%20training%20for%20use%20in%20clinical%20trials.jpeg?width=1000&name=Sample%20size%20and%20power%20training%20for%20use%20in%20clinical%20trials.jpeg "Sample size and power training for use in clinical trials") #### Webinar # **Sample Size and Power for Non-Parametric Analysis** ### **A guide to non-parametric analysis methods, tools & sample size considerations** ### This webinar has now ended Watch Now ** #### About the webinar Parametric tests, such as t-tests and ANOVA, are commonly used in clinical trials in order to establish the efficacy of candidate treatments. However, parametric tests require a number of assumptions to be met including the distribution of the data. In cases where these assumptions may not be met, researchers may consider semi-parametric tests and non-parametric tests which are robust in a wider variety of scenarios.  Semi-parametric methods such as Cox Regression and non-parametric tests such as Mann-Whitney U tests are therefore an essential tool for statistical analysis. Given this, there is a wide range of sample size methods available for finding the appropriate size for a trial using non-parametric analysis. In this free webinar, we cover the basics of non-parametric analysis, provide an overview of common non-parametric statistical methods used in clinical trials and introduce the sample size methods available for different types of non-parametric data. #### In this free webinar, you will learn about: ** More about the webinar   Parametric tests are commonly used in clinical trials in order to examine candidate treatments. However, these tests require that the underlying data satisfies certain assumptions.   For example, common methods such as t-tests and ANOVA are optimal when the data is continuous and normally distributed. In the cases where distributional or other assumptions are not met then semi-parametric and non-parametric tests may be useful.   Semi-parametric methods such as Cox Regression for survival data or proportional odds models and non-parametric tests such as Mann-Whitney U tests and quantile regression are therefore an essential tool for statistical analysis.   Common scenarios where non-parametric tests are useful are for ordinal (e.g. pain severity scales) and interval (e.g. Likert scales) data, where the outcome data has an order but is not continuous, and for complex continuous data such as when comparing data from fat-tailed distributions.    Given the wide range of scenarios considered for non-parametric tests, there is also a wide range of sample size methods available for finding the appropriate size for a trial. These methods can allow researchers to better explore the implications of their trial outcome on study power and feasibility.   In this free webinar, we cover non-parametric tests, non-parametric equivalents to popular parametric tests and practical examples of sample size determination for a selection of non-parametric tests including the Wilcoxon signed-rank test and the Wilcoxon-Mann-Whitney U tests for both continuous and ordinal outcomes.   ** #### Duration - 60 minutes **Speaker:** Denis Desmond, Research Statistician, nQuery ### Watch Now All info submitted is secure. 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