Models and Statistical Inference
The Controversy between Fisher and Neyman–Pearson
Bibliographic Data
| ID | 8397665 |
|---|---|
| Authors | Johannes Lenhard (0000-0001-7485-1578, Bielefeld University, corresponding author) |
| Year | 2006 |
| Volume | 57 |
| Issue | 1 |
| Pages | 69-91 |
| Publication date | 2006-03-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | The British Journal for the Philosophy of Science (JOURNAL) |
| Journal identifiers | ISSN: 0007-0882 • E-ISSN: 1464-3537 |
| Publisher | Oxford University Press (PUBLISHER • GB) |
| DOI | 10.1093/bjps/axi152 |
| OpenAlex | W1982941892 |
| Language | EN |
| Citations received | 8 |
| References cited | 19 |
The main thesis of the paper is that in the case of modern statistics, the differences between the various concepts of models were the key to its formative controversies. The mathematical theory of statistical inference was mainly developed by Ronald A. Fisher, Jerzy Neyman, and Egon S. Pearson. Fisher on the one side and Neyman–Pearson on the other were involved often in a polemic controversy. The common view is that Neyman and Pearson made Fisher's account more stringent mathematically. It is argued, however, that there is a profound theoretical basis for the controversy: both sides held conflicting views about the role of mathematical modelling. At the end, the influential programme of Exploratory Data Analysis is considered to be advocating another, more instrumental conception of models. 1. Introduction 2. Models in statistics—‘of what population is this a random sample?’ 3. The fundamental lemma 4. Controversy about models 5. Exploratory data analysis as a model-critical approach
Econometrics · Epistemology · Exploratory data analysis · Inference · Pearson product-moment correlation coefficient · Statistical hypothesis testing · Statistical inference · Statistical theory · Statistics · Data Analysis with R · Mathematics · Philosophy · Philosophy and History of Science · Statistics Education and Methodologies
Integrative data analysis
Significance Testing Needs a Taxonomy
Prior Information in Frequentist Research Designs
Alternative Model-Based and Design-Based Frameworks for Inference From Samples to Populations
Cohen’s convention, the seriousness of errors, and the body of knowledge in behavioral science
Justifying method choice
Perspectival realism and frequentist statistics
A transformation of Bayesian statistics
| Unique citing works | 8 |
|---|---|
| Citations per year | 0,47 |
| Citation span | 2009 - 2024 (16) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 8 |