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Jumping to conclusions (officially the jumping conclusion bias, often abbreviated as JTC, and also referred to as the inference-observation confusion [1]) is a psychological term referring to a communication obstacle where one "judge[s] or decide[s] something without having all the facts; to reach unwarranted conclusions".
Arbitrary inference is a classic tenet of cognitive therapy created by Aaron T. Beck in 1979. [1] He defines the act of making an arbitrary inference as the process of drawing a conclusion without sufficient evidence, or without any evidence at all.
Tendency to evaluate the logical strength of an argument based on current belief and perceived plausibility of the statement's conclusion. Framing: Tendency to narrow the description of a situation in order to guide to a selected conclusion. The same primer can be framed differently and therefore lead to different conclusions. Hindsight bias
It is a more extreme form of jumping-to-conclusions cognitive distortion where one presumes to know the thoughts, feelings, or intentions of others without any ...
Naturalistic fallacy – inferring evaluative conclusions from purely factual premises [105] [106] in violation of fact-value distinction. Naturalistic fallacy (sometimes confused with appeal to nature) is the inverse of moralistic fallacy. Is–ought fallacy [107] – deduce a conclusion about what ought to be, on the basis of what is.
A faulty generalization is an informal fallacy wherein a conclusion is drawn about all or many instances of a phenomenon on the basis of one or a few instances of that phenomenon. It is similar to a proof by example in mathematics. [1] It is an example of jumping to conclusions. [2]
Published in the journal Nature Climate Change, the paper reaches this conclusion via an unlikely route—analyzing six ... That doesn’t jump the long-term average over the 1.5-line, but it’s ...
By contrast, everyday reasoning is mostly non-monotonic because it involves risk: we jump to conclusions from deductively insufficient premises. We know when it is worth or even necessary (e.g. in medical diagnosis) to take the risk. Yet we are also aware that such inference is defeasible—that new information may undermine old conclusions.