Statistical distortion: Consequences of data cleaning

Tamraparni Dasu, Ji Meng Loh

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


We introduce the notion of statistical distortion as an essen-tial metric for measuring the effectiveness of data cleaning strategies. We use this metric to propose a widely applica-ble yet scalable experimental framework for evaluating data cleaning strategies along three dimensions: glitch improve-ment, statistical distortion and cost-related criteria. Exist-ing 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 compre-hensive suite of experiments and analyses.

Original languageEnglish (US)
Pages (from-to)1674-1683
Number of pages10
JournalProceedings of the VLDB Endowment
Issue number11
StatePublished - Jul 2012
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • General Computer Science


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