Error and the Growth of Experimental Knowledge

Deborah G. Mayo

ISBN 10: 0226511987 / ISBN 13: 9780226511986

Published by The University of Chicago Press, 1996

About this Item

This text provides a critique of the subjective Bayesian view of statistical inference, and proposes the author's own error-statistical approach as an alternative framework for the epistemology of experiment. It seeks to address the needs of researchers who work with statistical analysis. Series: Science & Its Conceptual Foundations S. Num Pages: 510 pages, 12 line drawings, 6 tables. BIC Classification: JHBC; PDA; PDN; PH. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 154 x 231 x 38. Weight in Grams: 782. . 1996. 1st Edition. Paperback. . . . . Books ship from the US and Ireland.

Seller Inventory # V9780226511986

Bibliographic Details

• Title: Error and the Growth of Experimental Knowledge
• Publisher: The University of Chicago Press
• Publication Date: 1996
• Language: English
• ISBN 10: 0226511987
• ISBN 13: 9780226511986
• Binding: Soft cover
• Condition: New

About this title

Synopsis

We may learn from our mistakes, but Deborah Mayo argues that, where experimental knowledge is concerned, we haven't begun to learn enough. Error and the Growth of Experimental Knowledge launches a vigorous critique of the subjective Bayesian view of statistical inference, and proposes Mayo's own error-statistical approach as a more robust framework for the epistemology of experiment. Mayo genuinely addresses the needs of researchers who work with statistical analysis, and simultaneously engages the basic philosophical problems of objectivity and rationality.

Mayo has long argued for an account of learning from error that goes far beyond detecting logical inconsistencies. In this book, she presents her complete program for how we learn about the world by being "shrewd inquisitors of error, white gloves off." Her tough, practical approach will be important to philosophers, historians, and sociologists of science, and will be welcomed by researchers in the physical, biological, and social sciences whose work depends upon statistical analysis.