Evidence for a proposition is what supports the proposition. It is usually understood as an indication that the supported proposition is true. What role evidence plays and how it is conceived varies from field to field.
In epistemology, evidence is what justifies beliefs or what makes it rational to hold a certain doxastic attitude. For example, a perceptual experience of a tree may act as evidence that justifies the belief that there is a tree. In this role, evidence is usually understood as a private mental state. Important topics in this field include the questions of what the nature of these mental states is, for example, whether they have to be propositional, and whether misleading mental states can still qualify as evidence.
In phenomenology, evidence is understood in a similar sense. Here, however, it is limited to intuitive knowledge that provides immediate access to truth and is therefore indubitable. In this role, it is supposed to provide ultimate justifications for basic philosophical principles and thus turn philosophy into a rigorous science. However, it is highly controversial whether evidence can meet these requirements.
In philosophy of science, evidence is understood as that which confirms or disconfirms scientific hypotheses. Measurements of Mercury's "anomalous" orbit, for example, are seen as evidence that confirms Einstein's theory of general relativity. In order to play the role of neutral arbiter between competing theories, it is important that scientific evidence is public and uncontroversial, like observable physical objects or events, so that the proponents of the different theories can agree on what the evidence is. This is ensured by following the scientific method and tends to lead to an emerging scientific consensus through the gradual accumulation of evidence. Two issues for the scientific conception of evidence are the problem of underdetermination, i.e. that the available evidence may support competing theories equally well, and theory-ladenness, i.e.
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On étudie des notions de topologie générale: unions et quotients d'espaces topologiques; on approfondit les notions de revêtements et de groupe fondamental,et d'attachements de cellules et on démontre
This MOOC is designed to help doctoral teaching assistants develop their teaching skills in scientific and engineering disciplines at university level. The MOOC will address what to do when you teach
This MOOC is designed to help doctoral teaching assistants develop their teaching skills in scientific and engineering disciplines at university level. The MOOC will address what to do when you teach
Originally, fallibilism (from Medieval Latin: fallibilis, "liable to err") is the philosophical principle that propositions can be accepted even though they cannot be conclusively proven or justified, or that neither knowledge nor belief is certain. The term was coined in the late nineteenth century by the American philosopher Charles Sanders Peirce, as a response to foundationalism. Theorists, following Austrian-British philosopher Karl Popper, may also refer to fallibilism as the notion that knowledge might turn out to be false.
Bayesian epistemology is a formal approach to various topics in epistemology that has its roots in Thomas Bayes' work in the field of probability theory. One advantage of its formal method in contrast to traditional epistemology is that its concepts and theorems can be defined with a high degree of precision. It is based on the idea that beliefs can be interpreted as subjective probabilities. As such, they are subject to the laws of probability theory, which act as the norms of rationality.
A proof is sufficient evidence or a sufficient argument for the truth of a proposition. The concept applies in a variety of disciplines, with both the nature of the evidence or justification and the criteria for sufficiency being area-dependent. In the area of oral and written communication such as conversation, dialog, rhetoric, etc., a proof is a persuasive perlocutionary speech act, which demonstrates the truth of a proposition.
Minimax-fair machine learning minimizes the error for the worst-off group. However, empirical evidence suggests that when sophisticated models are trained with standard empirical risk minimization (ERM), they often have the same performance on the worst-of ...
Professionals in the building design and operation fields typically look at standards and guidelines as a reliable source of information and guidance with regard to procedural, contractual, and legal scope and requirements that are relevant to accountabili ...
A relatively novel approach of autonomous navigation employing platform dynamics as the primary process model raises new implementational challenges. These are related to: (i) potential numerical instabilities during longer flights; (ii) the quality of mod ...