PPT-Designing Experiments: Sample Size and Statistical Power

Larry Leamy Department of Biology University of North Carolina at Charlotte Charlotte NC 28223 INTRODUCTION In designing experiments need to know what number of individuals would be optimal to detect differences between groups typically a control versus treatment groups. Study Design and Sample Size. Ideally we are involved in a study from the beginning. As statisticians (an epidemiologist) part of our role is to ensure the study is designed to address the primary hypothesis under consideration. Table of Contents. Overview of sampling in e-Discovery. Why sample? . Types of sampling. What is “statistically valid” sampling?. Mathematical considerations before sampling. How to sample in document review. Effect Size & Statistical Power. 1. 1. Effect Size. How “meaningful” is the significant difference?. 1. KNR 445. Statistics. Effect sizes. Slide . 3. Significance vs. meaningfulness. As sample size increases, likelihood of significant difference increases. Laura . Chioda. World Bank. 1. Outline. Ingredients of a general recipe called “Statistical Sampling Theory” . Key message. : a successful design involves some guess work. It is important to have a general rule, but then needs discussion depending on the case at hand. Dr. .. Richy Hetherington. and Dr. . Kim Pearce. Introductions . Today’s Session. Run a live Experiment . Discussion of considerations when setting up experiments. Analyse the results of our experiments with thoughts on what to look out . +. Principles of Simulation. Benjamin Neale. March 4. th. , 2010 . International Twin Workshop, Boulder, CO. Slide of Questions. What is power?. What affects power?. How do we calculate power?. What is simulation?. November 17, . 2015. Allen Kunselman, MA. Division of Biostatistics and Bioinformatics. Department of Public . H. ealth Sciences. Quotes. An approximate answer to the right question is worth a good deal more than an exact answer to the wrong question.. ) - 2012. J. Jack Lee, Ph.D.. Department of Biostatistics. University of Texas . M. D. Anderson Cancer Center. How many patients are needed in a clinical trial?. It depends on what you want to achieve.. Office of Methodological & Data Sciences. November . 13, 2015. Sarah Schwartz. www.cehs.usu.edu/research/omds. Quantitative Research. Research Question. Clear, focused & concise question that drives the study. Sample 4. Females. 43% . Work on management positions. 14%. Variable One. 22%. Variable Two. 31%. Variable Three. 34%. Variable Four. 39%. Variable Five. 2 in 5. Additional Descriptive statistics. 80%. Hao. . Zhou & David . Dueber. February 6, 2017. Applied Psychometric Strategies Lab. Applied Quantitative and Psychometric Series. Outline. Fake real life research scenario. Significance testing and statistical errors. Plan. Review of hypothesis testing. Power and sample size . Basic concepts. Formulae for common study designs. Using the software. When should you think about power & sample size?. Start thinking about statistics when you are planning your study. Margherita. . Polacci. F. . Arzilli, G. La Spina, N. Le . Gall. , . R. . Torres-. Orozco. , M. E. . Hartley. , D. Di Genova, . R. . C. . Atwood. , E. . W. . . Llewellin. , . R. . . Brooker. , H. M. . Quantitative Engineering Approaches. What do we know?. How do we design experiments and scale ?. Implications. Nothing except parameters we can vary. Statistical Experimental Design . Lots of experiments at all scales.