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The Future of Statistical Software Proceedings of a Forum (1991) / Chapter Skim
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Serendipitous Data and Future Statistical Software
Pages 15-22

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From page 15...
... Data from suitably designed and suitably randomized studies were the focus of data analysis based on these insights. However, much real-world data is serendipitous.
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From page 16...
... An important aspect of these advances is that, contrary to traditional methods, they do not require data from designed experiments or random samples; they can work with serendipitous data. By examining the differences in these two philosophies of statistical data analysis, we can see important trends in the future of statistics software.
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From page 17...
... utliers AnomaliesSubgroups \ _ ~ ~ Compare Fit Compute Residuals Transform\ FIGURE 2: Schematic of software to support scientific statistics.
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From page 18...
... to work with several programs, using each for what it does best and moving data and results among them freely. Thus, for example, one might open a favorite word processor, a presentation graphics program, a traditional mathematical statistics program, and a scientific data analysis program.
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From page 19...
... Finally, designing and programming for multiple platforms often means designing for the lowest common capabilities. What may be worse, many of the difficulties arising in writing portable software affect capabilities of particular interest to modern computer-based data analysis, such as the speed and look of graphics, methods of efficient and effective data management, and the design and implementation of the user interface.
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From page 20...
... We can reduce the need to re-invent. For someone with a new idea about data analysis or graphics, it can take an inordinate amount of time and effort to implement standard algorithms that are already known.
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From page 21...
... We need to recruit competent reviewers and encourage reviews that combine informed descriptions of a package's operation style, reasonable checks of its correctness, and well-designed studies of its performance. All complex software has bugs and errors.
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From page 22...
... Vetterling, 1986, Numencal Recipes, The Art of Scientific Computing, Cambridge University Press, New York.
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Key Terms

  • statistics software
  • mathematical statistics
  • serendipitous data
  • functional model
  • word processor

  • designed experiments
  • stochastic model
  • statistical data
  • support scientific
  • data analyst

  • innovative software
  • analysis program
  • data analyses
  • user interface
  • designed studies

  • software development
  • exploratory data
  • presentation graphics
  • analyses work
  • traditional statistics

  • copying results
  • hypothesis tests
  • classical statistics
  • data exploration
  • statistics program

  • graphics program
  • difficulties arising
  • interface foundation
  • data forms
  • traditional statistical


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