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In statistics education, informal inferential reasoning (also called informal inference) refers to the process of making a generalization based on data (samples) about a wider universe (population/process) while taking into account uncertainty without using the formal statistical procedure or methods (e.g. P-values, t-test, hypothesis testing, significance test).
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".
Abductive reasoning, also known as "inference to the best explanation", starts from an observation and reasons to the fact explaining this observation. An example is a doctor who examines the symptoms of their patient to make a diagnosis of the underlying cause. Analogical reasoning compares two similar systems. It observes that one of them has ...
The validity of an inference depends on the form of the inference. That is, the word "valid" does not refer to the truth of the premises or the conclusion, but rather to the form of the inference. An inference can be valid even if the parts are false, and can be invalid even if some parts are true.
These two definitions of formal logic are not identical, but they are closely related. For example, if the inference from p to q is deductively valid then the claim "if p then q" is a logical truth. [16] Formal logic needs to translate natural language arguments into a formal language, like first-order logic, to assess whether they are valid.
Anthropological survey paper from 1961 by Juhan Aul from University of Tartu who measured about 50 000 people. In fields such as epidemiology, social sciences, psychology and statistics, an observational study draws inferences from a sample to a population where the independent variable is not under the control of the researcher because of ethical concerns or logistical constraints.
Lastly, disguised observation raises some ethical issues regarding obtaining information without respondents' knowledge. For example, the observations collected by an observer participating in an internet chat room discussing how racists advocate racial violence may be seen as incriminating evidence collected without the respondents' knowledge.
A causal relationship between the observations and hypothesis does not exist to cause the observation to be taken as evidence, [3] but rather the causal relationship is provided by the person seeking to establish observations as evidence. A more formal method to characterize the effect of background beliefs is Bayesian inference. [5]