Producing wrong data without doing anything obviously wrong! 论文
2009引用 294
Data Visualization and AnalyticsEvolutionary Algorithms and ApplicationsAdvanced Database Systems and Queries
摘要
This paper presents a surprising result: changing a seemingly innocuous aspect of an experimental setup can cause a systems researcher to draw wrong conclusions from an experiment. What appears to be an innocuous aspect in the experimental setup may in fact introduce a significant bias in an evaluation. This phenomenon is called measurement bias in the natural and social sciences.