Abstract
We may learn from our mistakes, but this work argues that, where experimental knowledge is concerned, we haven't begun to learn enough. It provides a critique of the subjective Bayesian view of statistical inference, and proposes the author's own error-statistical approach as a more robust framework for the epistemology of experiment. Deborah Mayo seeks to address the needs of researchers who work with statistical analysis, and simultaneously engages the basic philosophical problems of objectivity and rationality. Mayo has argued for an account of learning from error that goes beyond detecting logical inconsistencies. In this book, she presents her complete programme for how we learn about the world by being shrewd inquisitors of error, white gloves off. Her approach should be relevant to philosophers, historians and sociologists of science, as well as researchers in the physical, biological and social sciences whose work depends upon statistical analysis.
Keywords
Objectivity (philosophy)RationalityEpistemologyStatistical inferenceInferencePhilosophy of scienceBayesian probabilitySociologyComputer scienceArtificial intelligencePhilosophyMathematicsStatistics
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Publication Info
- Year
- 1996
- Type
- book
- Volume
- 15
- Issue
- 1
- Citations
- 699
- Access
- Closed
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Cite This
Deborah G. Mayo
(1996).
Error and the Growth of Experimental Knowledge.
, 15
(1)
.
https://doi.org/10.7208/chicago/9780226511993.001.0001
Identifiers
- DOI
- 10.7208/chicago/9780226511993.001.0001