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The Logic of Hypothesis Testing - Forrest W. Young

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Hypothesis Testing, Logic of - SAGE Research Methods

Abstract. Hypothesis theory for nonmonotonic reasoning expresses notions of hypotheses and of known information. In this paper, we de ne these notions in the framework of a new modal system with two modalities, one for exprressing known information and the other for expressing possible hypotheses. A complete characterization of the new logic is given in terms of Kripke semantics. Moreover, our logic allows to characterize completely default logic, including a necessary and su cient criterium for the existence and the non-existence of extensions. We also present a notion of nonmonotonic inference which iscumulative. 1

From here, according to Fodor and Pylyshyn, establishing thesystematicity of thought as a nomological fact is one step away. If itis a law that the ability to understand a sentence is systematicallyconnected to the ability to understand many others, then it issimilarly a law that the ability to think a thought is systematicallyconnected to the ability to think many others. For to understand asentence is just to think the thought/proposition it expresses. Since,according to RTM, to think a certain thought is just to token arepresentation in the head that expresses the relevant proposition,the ability to token certain representations is systematicallyconnected to the ability to token certain others. But then, this factneeds an adequate explanation too. The classical explanation LOTHoffers is to postulate a system of representations with combinatorialsyntax exactly as in the case of the explanation of the linguisticsystematicity. This is what (B1) offers.[] This seems to be the only explanation that does not make thesystematicity of thought a miracle, and thus argues for the LOThypothesis.

Joe Schmuller will immerse you in the logic of hypothesis testing

Joe Schmuller will immerse you in the logic of hypothesis testing. Joe explains how hypothesis testing is a process for reasoning about data and will help you distinguish between ordinary and extraordinary data. After defining both null hypothesis and alternative hypothesis, you will calculate how many people have a higher than average IQ for a particular zip code.

- [Voiceover] Let's get into the logic…of hypothesis testing.…Hypothesis testing is a way of reasoning about data.…We typically have to decide…if our data are ordinary or extraordinary.…A hypothesis is a tentative explanation…of some process or outcome.…The null hypothesis is a tentative explanation…that says that what you've observed represents…nothing new or nothing out of the ordinary.…The alternative hypothesis is a tentative explanation…that says what you've observed represents…something new and something that is important.…

Adaptation hypothesis logic and evidence Language is …

The crux of the issue seems to be that learning concepts is a rational process. There seem to be non-arbitrary semantic and epistemic liaisons between the target concept to be acquired and its “evidence” base. This evidence base needs to be represented and rationally tied to the target concept. This target concept needs also to be expressed in terms of representations one already possesses. Fodor thinks that any model of concept learning understood in this sense will have to be a form of hypothesis formation and confirmation. But not every form of concept acquisition is learning. There are non-rational ways of acquiring concepts whose explanation need not be at the cognitive level (e.g., brute triggering mechanisms that can be activated in sorts of ways that can presumably be explained at the sub-cognitive or neurophysiological levels). If concepts cannot be learned, then they are either innate or non-rationally acquired. Whereas early Fodor used to think that concepts must therefore be innate (maybe he thought that non-learning concept acquisition forms are limited to sensory or certain classes of perceptual concepts), he now thinks that they may be acquired but the explanation of this is not the business of cognitive psychology.

Since the Research Hypothesis cannot be proven, the next logical step is to see if we can fail to it. The Research Hypothesis (H1) is IF A THEN B (i.e. there is a relationship between A and B). The opposite, or Null Hypothesis (H0), is that there is . This is the logical NOT of A B.

• The Logic of Hypothesis Testing Flashcards | Quizlet

Adaptation hypothesis logic and evidence Language is too well designed for from PSYC 358 at Binghamton

• The logic of null hypothesis testing - Cambridge Core

02/04/1998 · The logic of null hypothesis testing - Volume 21 Issue 2 - Edward Erwin

• Describe the basic logic of hypothesis testing

Logic Of Hypothesis Testing | Researchomatic

Logic of Hypothesis Testing 3-2 - Term Paper Champions

P.P.S.: Just for clarification after reading the answers so far: If you accept scientific theory, that you can only falsify statements but not prove them, the only thing that is logically consistent is choosing the null hypothesis as the new theory - which can then be falsified. Because if you falsify the status quo you are left empty handed (the status quo is disproved but the new theory far from being proved!). And if you fail to falsify it you are in no better position either.

Hypothesis testing: Double negative logic, five step …

The two most important achievements of 20th century that are at thefoundations of LOTH as well as most of modern Artificial Intelligence(AI) research and most of the so-called information processingapproaches to cognition are (i) the developments in modern symbolic(formal) logic, and (ii) Alan Turing's idea of a Turing Machine andTuring computability. It is putting these two ideas together thatgives LOTH its enormous explanatory power within a naturalisticframework. Modern logic showed that most of deductive reasoning can beformalized, i.e. most semantic relations among symbols can beentirely captured by the symbols' formal/syntactic properties and therelations among them. And Turing showed, roughly, that if a processhas a formally specifiable character then it can be mechanized. So wecan appreciate the implications of (i) and (ii) for the philosophy ofpsychology in this way: if thinking consists in processingrepresentations physically realized in the brain (in the way theinternal data structures are realized in a computer) and theserepresentations form a formal system, i.e., a language with its propercombinatorial syntax (and semantics) and a set of derivations rulesformally defined over the syntactic features of those representations(allowing for specific but powerful programs to be writtenin terms of them), then the problem of thinking, as describedabove, can in principle be solved in completely naturalistic terms,thus the mystery surrounding how a physical device can ever havesemantically coherent state transitions (processes) can beremoved. Thus, given the commitment to naturalism, the hypothesis thatthe brain is a kind of computer trafficking in representations invirtue of their syntactic properties is the basic idea of LOTH (andthe AI vision of cognition).

Hypothesis Contrary to Fact - Logically Fallacious

P.S.: You cannot even negate the above hypothesis to get a valid equivalent hypothesis, so you cannot say "The drug is not effective" as a null hypothesis because the only logically equivalent form would be "if you see no effect the drug will not be effective" which brings you nowhere because now the conclusion is what you want to find out!

logic - Is continuum hypothesis decidable in second …

When Fodor first formulated LOTH with significant elaboration in his(1975), he introduced his major argument for it along with its initialformulation in the first chapter. It was basically this: our bestscientific theories and models of different aspects of highercognition assume a framework that requires acomputational/representational medium for them to be true. Morespecifically, he analyzed the basic form of the information processingmodels developed to account for three types of cognitive phenomena:perception as the fixation of perceptual beliefs, conceptlearning as hypothesis formation and confirmation, anddecision making as a form of representing and evaluating theconsequences of possible actions carried out in a situation with apreordered set of preferences. He rightly pointed out that all thesepsychological models treated mental processes as computational processes definedover representations. Then he drew what seems to be the obviousconclusion: if these models are right in at least treating mentalprocesses as computational, even if not in detail, then there must bea LOT over which they are defined, hence LOTH.

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