Focus on Data: Statistical Design of Experiments and Sample Size Selection Using Power Analysis.

Abstract

PurposeTo provide information to visual scientists on how to optimally design experiments and how to select an appropriate sample size, which is often referred to as a power analysis.MethodsStatistical guidelines are provided outlining good principles of experimental design, including replication, randomization, blocking or grouping of subjects, multifactorial design, and sequential approach to experimentation. In addition, principles of power analysis for calculating required sample size are outlined for different experimental designs and examples are given for calculating power and factors influencing it.ResultsThe interaction between power, sample size and standardized effect size are shown. The following results are also provided: sample size increases with power, sample size increases with decreasing detectable difference, sample size increases proportionally to the variance, and two-sided tests, without preference as to whether the mean increases or decreases, require a larger sample size than one-sided tests.ConclusionsThis review outlines principles for good experimental design and methods for power analysis for typical sample size calculations that visual scientists encounter when designing experiments of normal and non-Gaussian sample distributions.