# Belief, Evidence, and UncertaintyBayesian and Evidential Paradigms

Belief, Evidence, and Uncertainty: Bayesian and Evidential Paradigms [The first step is to distinguish two questions: Given the data, what should we believe, and to what degree?What kind of evidence do the data provide for a hypothesis H1 as against an alternative hypothesis H2, and how much?We call the first the “confirmation”, the second the “evidence” question. Many different answers to each have been given. In order to make the distinction between them as intuitive and precise as possible, we answer the first in a Bayesian way: a hypothesis is confirmed to the extent that the data raise the probability that it is true. We answer the second question in a Likelihoodist way, that is, data constitute evidence for a hypothesis as against any of its rivals to the extent that they are more likely on it than on them. These two simple ideas are very different, but both can be made precise, and each has a great deal of explanatory power. At the same time, they enforce corollary distinctions between “data” and “evidence”, and between different ways in which the concept of “probability” is to be interpreted. An Appendix explains how our likelihoodist account of evidence deals with composite hypotheses.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

# Belief, Evidence, and UncertaintyBayesian and Evidential Paradigms

Part of the SpringerBriefs in Philosophy Book Series
21 pages

Publisher
Springer International Publishing
© The Author(s) 2016
ISBN
978-3-319-27770-7
Pages
15 –36
DOI
10.1007/978-3-319-27772-1_2
Publisher site
See Chapter on Publisher Site

### Abstract

[The first step is to distinguish two questions: Given the data, what should we believe, and to what degree?What kind of evidence do the data provide for a hypothesis H1 as against an alternative hypothesis H2, and how much?We call the first the “confirmation”, the second the “evidence” question. Many different answers to each have been given. In order to make the distinction between them as intuitive and precise as possible, we answer the first in a Bayesian way: a hypothesis is confirmed to the extent that the data raise the probability that it is true. We answer the second question in a Likelihoodist way, that is, data constitute evidence for a hypothesis as against any of its rivals to the extent that they are more likely on it than on them. These two simple ideas are very different, but both can be made precise, and each has a great deal of explanatory power. At the same time, they enforce corollary distinctions between “data” and “evidence”, and between different ways in which the concept of “probability” is to be interpreted. An Appendix explains how our likelihoodist account of evidence deals with composite hypotheses.]

Published: Mar 5, 2016

Keywords: Confirmation; Evidence; Bayesianism; Likelihoods; Interpretations of probability; Absolute and incremental confirmation; Lottery paradox; Composite hypotheses

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