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Refining the Concept of Scientific Inference When Working with Big Data Proceedings of a Workshop—in Brief (2016) / Chapter Skim
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Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop-in Brief
Pages 1-4

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From page 1...
... health records, Sebastien Haneuse (Harvard University) encouraged researchers to Initially analysts were unaware of this confounding effect, but sorting data compare opportunistic data to that which would result from a dedicated study de- based on the date supplied by experisigned to answer the question at hand and to use this comparison as a guidepost mental collaborators shows a clear batch effect (left grouping is after inin assessing the suitability of available data.
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From page 2...
... Andrew Nobel emphasized the importance community, he encouraged the development and dis- of teaching statistics students at all levels the basics of semination of new diagnostic tools that can be used eas- statistical computing, database management, and data ily by those without extensive training in statistics such sharing practices that are increasingly important with as bench scientists and policy analysts. Elizabeth Stuart Big Data.
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From page 3...
... EARLY PARTICIPATION AND COLLABORA- equivocation means a lot more than somebody else's TION IN EXPERIMENTAL DESIGN confidence." Concern regarding the perception of statisXihong Lin encouraged statisticians to consider them- ticians as overly negative was shared by Alfred Hero, who selves scientists, as opposed to analysts on the periph- urged statisticians not just to challenge the significance ery of scientific discovery. She pointed to biostatistics of inferences drawn from large, complex data, but also as an example of a field with strong collaboration be- to suggest experimental strategies that would improve tween statisticians and domain scientists, which has led confidence in findings under uncertainty.
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From page 4...
... described the possibility that many aspects of statistical In the case of electronic health records, Bin Yu encour- analysis could become fully automated in the future, and aged federal agencies to facilitate data sharing by hos- she said that the community must make sure appropriate pitals and healthcare providers, as many insights could techniques are implemented in these emerging analysis be gained by linking datasets and comparing popula- packages. PLANNING COMMITTEE: Michael J
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Key Terms

  • alfred hero
  • genevera allen
  • data science
  • scientific inference
  • andrew nobel

  • health records
  • complex datasets
  • robert kass
  • xihong lin
  • data sharing

  • scientific domains
  • statistical modeling
  • statistical models
  • electronic health
  • brown university

  • confirmatory analyses
  • michael kosorok
  • mental collaborators
  • anderson cancer
  • cosma shalizi

  • encouraged statisticians
  • rice university
  • teaching statistics
  • concern regarding
  • theoretical statistics

  • pel hill
  • cancer center
  • baylor college
  • complex data
  • multiscale data


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