Funding agencies and journals consider rigor and reproducibility as major criteria for funding and publication. In addition, the size of the effect anticipated should be of practical importance and the experimental design should ensure reproducibility of results. A power analysis is of great importance when planning an experiment that has a reasonably good chance of detecting treatment effects if they exist. This tutorial in the “Focus on Data” series provides information on how to optimally design experiments and how to select an appropriate sample size, which is often referred to as a power analysis. This 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. 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. The interaction between power, sample size and standardized effect size are shown. 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. Statistical guidelines are provided outlining good principles of experimental design, including replication, randomization, blocking or grouping of subjects, multifactorial design, and sequential approach to experimentation. To 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.
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