## Error and the Growth of Experimental KnowledgeWe 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. |

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### Contents

Learning from Error | 1 |

Ducks Rabbits and Normal Science Recasting the KuhnsEye View of Popper | 21 |

The New Experimentalism and the Bayesian Way | 57 |

Duhem Kuhn and Bayes | 102 |

Models of Experimental Inquiry | 128 |

Severe Tests and Methodological Underdetermination | 174 |

The Experimental Basis from Which to Test Hypotheses Brownian Motion | 214 |

Severe Tests and Novel Evidence | 251 |

Hunting and Snooping Understanding the NeymanPearson Predesignationist Stance | 294 |

Why You Cannot Be Just a Little Bit Bayesian | 319 |

Why Pearson Rejected the NeymanPearson Behavioristic Philosophy and a Note on Objectivity in Statistics | 361 |

Error Statistics and Peircean Error Correction | 412 |

Toward an ErrorStatistical Philosophy of Science | 442 |

465 | |

481 | |

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### Common terms and phrases

accept actual alternative appraisal argue argument from error assign Bayes's theorem Bayesian Binomial Brownian motion calculated canonical models ceteris paribus chance chapter consider correlation criticism data models deflection degrees of belief discussion distribution eclipse effect error probabilities error statistician error statistics estimate evidence example experiment experimental knowledge experimental model experimental testing factors false frequentist Giere given Howson and Urbach hypothe hypothesis H induction inquiry Kuhn Kuhn's likelihood likelihood principle mean ment methods muons Neyman non-Bayesian normal science normal testing novelty NP tests null hypothesis observed outcome paradigm parameter passed a severe Pearson Peirce Peirce's Perrin philosophy of science Popper Popperian posterior probability predesignation prediction primary prior problem question random relative frequency reliable requirement result Salmon sample scientific inference scientists severe test significance level specific standard error statistics statistical tests statistically significant stopping rule success theory tion trials true type I error use-constructed violating