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Statistical data analysis in the computer age.

B Efron, R Tibshirani

    Science (New York, N.Y.)
    |July 26, 1991
    PubMed
    Summary
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    Modern computational power enables new statistical methods, reducing reliance on strict distributional assumptions for complex data analysis and inference. Computer algorithms now drive statistical modeling, replacing traditional mathematical tractability.

    Area of Science:

    • Statistics
    • Computational Statistics
    • Data Science

    Background:

    • Traditional statistical methods (hypothesis testing, regression, ANOVA, maximum likelihood estimation) were developed for mechanical calculators.
    • These methods often require strict distributional assumptions and can be mathematically intractable for complex problems.

    Purpose of the Study:

    • To highlight the impact of modern electronic computation on statistical methodology.
    • To introduce advanced statistical techniques that overcome limitations of older methods.

    Main Methods:

    • Exploration of new statistical methods enabled by electronic computation.
    • Application of computer algorithms to replace traditional mathematical analysis for statistical inference.
    • Utilizing algorithms for data exploration, description, and valid statistical inference.

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    Main Results:

    • New statistical methods require fewer distributional assumptions.
    • These methods are applicable to more complex statistical estimators.
    • Scientists can explore and infer from data without typical mathematical tractability concerns.

    Conclusions:

    • Electronic computation has revolutionized statistical practice, enabling more flexible and powerful data analysis.
    • Computer algorithms are essential for developing and validating modern statistical inference tools.
    • Mathematics remains crucial for ensuring the correctness and efficiency of automated statistical algorithms.