Confirmation bias among middle school students prepared

The current research aims to know the confirmation bias among middle school students, as well as knowing the significance of the differences in the confirmation bias among middle school students according to the variables of sex (males, females, gender, gender, gender, gender, They adopted the researcher scale (Shannan,2019), depending on the theory Wason consisted measure in its final form after the completion of the terms of honesty, fortitude and strength discriminatory internal consistency (28) paragraph, was the application of the tool on a sample of (500) students, were tested way The random stratification with a proportional style, for the year (2020-2021), and the research net showed that middle school students commonly have confirmation bias. • There are no statistically significant differences in the degrees of confirmation bias among middle school students, according to gender variables (Nakor, female). • There are statistically significant differences in the confirmation bias according to the specialization variable (scientific - literary) and towards the scientific specialization. • There are statistically significant differences in the degree of confirmation bias between students of the fourth and fifth grades and towards the fourth grade. • There are differences in the confirmation bias as a result of the interactions between the variables of gender (male, female), specialization (scientific, literary), and grade (fourth, fifth).

Metalogical Remarks on Induction

The problem of induction belongs to the most controversial issues in philosophy of science. If induction is understood widely, it covers every fallible inference, that is, such that its conclusion is not logically entailed by its premises. This paper analyses so-called reductive induction, that is, reasoning in which premises follow from the conclusion, but the reverse relation does not hold. Two issues are taken into account, namely the definition of reductive inference and its justification. The analysis proposed in the paper employs metalogical tools. The author agrees with the view that a quantitative account of degree of confirmation for universal theories via logical probability is problematic. However, prospect for a qualitative approach look as more promising. Using the construction of maximally consistent sets allows to distinguish good and worthless induction as well as shows how to understand induction in a semantic way. A closer analysis of deductivism in the theory of justification shows that it is a hidden inductivism.

The epistemology of the SARS-CoV-2 test

We investigate the epistemological consequences of a positive SARS-CoV-2 test for two relevant hypotheses: (i) V is the hypothesis that an individual has been infected with SARS-CoV-2; (ii) C is the hypothesis that SARS-CoV-2 is the sole cause of flu-like symptoms in a given patient. We ask two fundamental epistemological questions regarding each hypothesis: First, given a positive SARS-CoV-2 test, what should we believe about the hypothesis and to what degree? Second, how much evidence does a positive test provide for a hypothesis against its negation? We respond to each question within a formal Bayesian framework. We construe degree of confirmation as the difference between the posterior probability of the hypothesis and its prior, and the strength of evidence for a hypothesis against its alternative in terms of their likelihood ratio. We find that for realistic assumptions about the base rate of infected individuals, P(V)≲20%, positive tests having low specificity (75%) would not raise the posterior probability for V to more than 50%. Furthermore, if the test specificity is less than 88.1%, even a positive test having 95% sensitivity would only yield weak to moderate evidence for V against ¬V. We also find that in plausible scenarios, positive tests would only provide weak to moderate evidence for C unless the tests have a high specificity. One has thus to be careful in ascribing the symptoms or death of a positively tested patient to SARS-CoV-2, if the possibility exists that the disease has been caused by another pathogen.

The No Alternatives Argument

Convincing scientific theories are often hard to find, especially when empirical evidence is scarce (e.g., in particle physics). Once scientists have found a theory, they often believe that there are not many distinct alternatives to it. Is this belief justified? We model how the failure to find a feasible alternative can increase the degree of belief in a scientific theory—in other words, we establish the validity of the No Alternatives Argument and the possibility of non-empirical theory confirmation from a Bayesian point of view. Then we evaluate scope and limits of this argument (e.g., by calculating the degree of confirmation it provides) and relate it to other argument forms such as Inference to the Best Explanation (IBE) or “There is No Alternative” (TINA).

Bayesian Philosophy of Science

“Bayesian Philosophy of Science” addresses classical topics in philosophy of science, using a single key concept—degrees of beliefs—in order to explain and to elucidate manifold aspects of scientific reasoning. The basic idea is that the value of convincing evidence, good explanations, intertheoretic reduction, and so on, can all be captured by the effect it has on our degrees of belief. This idea is elaborated as a cycle of variations about the theme of representing rational degrees of belief by means of subjective probabilities, and changing them by a particular rule (Bayesian Conditionalization). Partly, the book is committed to the Carnapian tradition of explicating essential concepts in scientific reasoning using Bayesian models (e.g., degree of confirmation, causal strength, explanatory power). Partly, it develops new solutions to old problems such as learning conditional evidence and updating on old evidence, and it models important argument schemes in science such as the No Alternatives Argument, the No Miracles Argument or Inference to the Best Explanation. Finally, it is explained how Bayesian inference in scientific applications—above all, statistics—can be squared with the demands of practitioners and how a subjective school of inference can make claims to scientific objectivity. The book integrates conceptual analysis, formal models, simulations, case studies and empirical findings in an attempt to lead the way for 21th century philosophy of science.

Confirmation and Induction

Confirmation of scientific theories by empirical evidence is an important element of scientific reasoning and a central topic in philosophy of science. Bayesian Confirmation Theory—the analysis of confirmation in terms of degree of belief—is the most popular model of inductive reasoning. It comes in two varieties: confirmation as firmness (of belief), and confirmation as increase in firmness. We show why increase in firmness is a particularly fruitful explication of degree of confirmation, and how it resolves longstanding paradoxes of inductive inference (e.g., the paradox of the ravens, the tacking paradoxes and the grue paradox). Finally, we give an axiomatic characterization of various confirmation measures and we discuss the question of whether there is a single adequate measure of confirmation or whether a pluralist position is more promising

Expressivism and Propositions

A defense of a version of Allan Gibbard’s expressivist analysis of normative judgments, focusing on his account of what he calls “normative logic.” The version defended interprets his analysis in a way that is significantly different from his own interpretation, which ties expressivism to a deflationary notion of truth. It is argued that Gibbard’s general account blurs the line between expressivism and normative realism, and that a more robust notion of truth that draws a sharper line can be defended, and can be reconciled with his normative logic. The chapter concludes by considering the application of this expressivist account to epistemic norms, and more specifically to norms for assessing degrees of belief and measures of degree of confirmation.

Explication, Description and Enlightenment

In the first chapter of his book Logical Foundations of Probability, Rudolf Carnap introduced and endorsed a philosophical methodology which he called the method of ‘explication’. P.F. Strawson took issue with this methodology, but it is currently undergoing a revival. In a series of articles, Patrick Maher has recently argued that explication is an appropriate method for ‘formal epistemology’, has defended it against Strawson’s objection, and has himself put it to work in the philosophy of science in further clarification of the very concepts on which Carnap originally used it (degree of confirmation, and probability), as well as some concepts to which Carnap did not apply it (such as justified degree of belief). We shall outline Carnap’s original idea, plus Maher’s recent application of such a methodology, and then seek to show that the problem Strawson raised for it has not been dealt with. The method is indeed, we argue, problematic and therefore not obviously superior to the ‘descriptive’ method associated with Strawson. Our targets will not only be Carnapians, though, for what we shall say also bears negatively on a project that Paul Horwich has pursued under the name ‘therapeutic’, or ‘Wittgensteinian’ Bayesianism. Finally, explication, as we shall suggest and as Carnap recognised, is not the only route to philosophical enlightenment.

PROCEDURE FOR QUANTITATIVE ESTIMATION OF CONFIRMATION OF THE PREDICTION BY THE STRUCTURAL 3D-MODEL FOR HORIZONTALSECTION OF WELLS

This article considers the problems of estimation of the structural model verification based on the results of the well horizontal section drilling. The method of estimation by standard criteria of the structure confirmation is presented as well as the procedure is described in which the section of the horizontal well is considered as population of data. Additionally a set of parameters is calculated which give a differentiated estimate and a quantitative expression for the degree of confirmation of the well horizontal section model.

High Confirmation and Inductive Validity

Does a high degree of confirmation make an inductive argument valid? I will argue that it depends on the kind of question to which the argument is meant to be providing an answer. We should distinguish inductive generalization from inductive extrapolation even in cases where they might appear to have the same answer, and also from confirmation of a hypothesis.