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dc.contributor.authorHoon, Niels H. L. C. deen_US
dc.contributor.authorEisemann, Elmaren_US
dc.contributor.authorVilanova, Annaen_US
dc.contributor.editorKai Lawonn and Noeska Smit and Douglas Cunninghamen_US
dc.date.accessioned2017-06-12T05:15:29Z
dc.date.available2017-06-12T05:15:29Z
dc.date.issued2017
dc.identifier.isbn978-3-03868-041-3
dc.identifier.urihttp://dx.doi.org/10.2312/eurorv3.20171110
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/eurorv320171110
dc.description.abstractThe evaluation of visualization methods or designs often relies on user studies. Apart from the difficulties involved in the design of the study itself, the existing mechanisms to obtain sound conclusions are often unclear. In this work, we review and summarize some of the common statistical techniques that can be used to validate a claim in the scenarios that are commonly present in user studies in visualization, i.e., hypothesis testing. Usually, the number of participants is small and the mean and variance of the distribution are not known. Therefore, we will focus on the techniques that are adequate within these limitations. Our aim for this paper is to clarify the goals and limitations of hypothesis testing from a user study perspective, that can be interesting for the visualization community. We provide an overview of the most common mistakes made when testing a hypothesis that can lead to erroneous claims. We also present strategies to avoid those.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectG.3 [Mathematics of Computing]
dc.subjectProbability and Statistics
dc.subjectExperimental Design
dc.titleFrom a User Study to a Valid Claim: How to Test Your Hypothesis and Avoid Common Pitfallsen_US
dc.description.seriesinformationEuroVis Workshop on Reproducibility, Verification, and Validation in Visualization (EuroRV3)
dc.description.sectionheadersEvaluation Guidelines
dc.identifier.doi10.2312/eurorv3.20171110
dc.identifier.pages25-28


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