  We introduce the notion of statistical distortion as an essential metric for
measuring the effectiveness of data cleaning strategies. We use this metric to
propose a widely applicable yet scalable experimental framework for evaluating
data cleaning strategies along three dimensions: glitch improvement,
statistical distortion and cost-related criteria. Existing metrics focus on
glitch improvement and cost, but not on the statistical impact of data cleaning
strategies. We illustrate our framework on real world data, with a
comprehensive suite of experiments and analyses.
