scholarly journals The Sure-Thing Principle

2016 ◽  
Vol 4 (1) ◽  
pp. 81-86 ◽  
Author(s):  
Judea Pearl
Keyword(s):  
Statistical Inference ◽  
Causal Reasoning ◽  
History Of ◽  
Sure Thing Principle

AbstractIn 1954, Jim Savage introduced the Sure Thing Principle to demonstrate that preferences among actions could constitute an axiomatic basis for a Bayesian foundation of statistical inference. Here, we trace the history of the principle, discuss some of its nuances, and evaluate its significance in the light of modern understanding of causal reasoning.

Searching for a Tide Table for Business:

2021 ◽  
Vol 53 (6) ◽  
pp. 139-174
Author(s):  
Laetitia Lenel
Keyword(s):  
Statistical Inference ◽  
Historical Context ◽  
The United States ◽  
Economic Research ◽  
Inside Information ◽  
Mechanical Working ◽  
Cast Doubt ◽  
Business Forecasting ◽  
History Of ◽  
General Business

The article investigates the methods and conceptions of statistical inference used in business forecasting in the United States and in Europe in the 1920s. After presenting the methods and arguments used by the members of the Harvard Committee on Economic Research in the first years after its establishment in 1919, the article explores the far-reaching changes in method and conviction from 1922 on. The members’ realization that the future evolved differently than predicted prompted them to give up their hope for mechanical means of forecasting and to revoke their calls for the employment of the mathematical theory of probability in economics. Instead, they established an extensive correspondence with economic and political decision-makers that allowed them to base their forecasts on “inside information.” Subsequently, the article traces European attempts to adopt the Harvard Index of General Business Conditions in the early 1920s. Impressed by the seemingly mechanical working of the Harvard index, European economists and statisticians sought to establish similar indices for their countries. However, numerous revisions of the Harvard index in the mid-1920s cast doubt on the universality of the index and the existence of stable patterns and led European researchers to pursue different paths of investigation. The article complicates the larger history of statistical inference in economics in two meaningful ways. First, it argues that statistical inference with probability was not the long-sought solution for the problem of objectivity but a long-contested, and repeatedly discarded, approach. Second, it shows that these contestations were often triggered by deviations between forecasts and the conditions actually observed and by this means argues for the importance of the historical context in the history of economics.

scholarly journals Petitions in scientific argumentation: dissecting the request to retire statistical significance

2019 ◽  
Author(s):  
Tom Elis Hardwicke ◽  
john Ioannidis
Keyword(s):  
Statistical Inference ◽  
Random Sample ◽  
Statistical Significance ◽  
Scientific Argumentation ◽  
History Of ◽  
Substantial Heterogeneity ◽  
Scientific Fields

KEY MESSAGES•Petitions have a long history of being used for political, social, ethical, and injustice issues, however, it is unclear how/whether they should be implemented in scientific argumentation. •Recently, an extremely influential commentary published in Nature (Amrhein et al., 2019) calling for the abandonment of “statistical significance” was signed by 854 scientists. •We surveyed signatories and observed substantial heterogeneity in respondents’ perceptions of the petition process, motivations for signing, and views on aspects of abandoning statistical significance. •The top-cited signatories were strongly concentrated in a few scientific fields.•In a random sample of 100 signatories, 62 published at least one paper in 2018 using statistical inference and most of them had used the phrase “statistical significance”. •When scientists sign petitions, they may have variable views on important aspects and it is useful to understand this diversity.

Exploring the History of Statistical Inference in Economics

2021 ◽  
Vol 53 (6) ◽  
pp. 1-24
Author(s):  
Jeff Biddle ◽  
Marcel Boumans
Keyword(s):  
Statistical Inference ◽  
History Of

Using Genomic Data to Infer Historic Population Dynamics of Nonmodel Organisms

2018 ◽  
Vol 49 (1) ◽  
pp. 433-456 ◽  
Author(s):  
Annabel C. Beichman ◽  
Emilia Huerta-Sanchez ◽  
Kirk E. Lohmueller
Keyword(s):  
Population Dynamics ◽  
Statistical Inference ◽  
Statistical Methods ◽  
Genome Sequence ◽  
Sequence Data ◽  
Demographic History ◽  
Genomic Data ◽  
Underlying Theory ◽  
History Of ◽  
Pros And Cons

Genome sequence data are now being routinely obtained from many nonmodel organisms. These data contain a wealth of information about the demographic history of the populations from which they originate. Many sophisticated statistical inference procedures have been developed to infer the demographic history of populations from this type of genomic data. In this review, we discuss the different statistical methods available for inference of demography, providing an overview of the underlying theory and logic behind each approach. We also discuss the types of data required and the pros and cons of each method. We then discuss how these methods have been applied to a variety of nonmodel organisms. We conclude by presenting some recommendations for researchers looking to use genomic data to infer demographic history.

scholarly journals Statistical Inference for the Evolutionary History of Cancer Genomes

2020 ◽  
Vol 35 (1) ◽  
pp. 129-144 ◽  
Author(s):  
Khanh N. Dinh ◽  
Roman Jaksik ◽  
Marek Kimmel ◽  
Amaury Lambert ◽  
Simon Tavaré
Keyword(s):  
Statistical Inference ◽  
Evolutionary History ◽  
Cancer Genomes ◽  
History Of ◽  
History Of Cancer
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