Inference, a mental process, entails forming conclusions from existing evidence and logical reasoning. It’s an integral aspect of critical thinking and problem-solving, with wide-ranging applications in areas such as scientific investigation, literary analysis, and artificial intelligence[1]. Various forms of inference exist, such as deductive, inductive, abductive, statistical, and causal, each with its distinctive method and purpose. For example, deductive inference focuses on reaching specific conclusions from broad principles, whereas inductive inference generates broad conclusions from specific instances. Conversely, abductive inference involves making informed assumptions based on accessible evidence, while statistical and causal inferences revolve around interpreting data to make conclusions about a group or to establish cause-and-effect connections. Nonetheless, the precision of inferences can be affected by biases, preconceived notions, and misinterpretations. Despite these potential obstacles, enhancing inference skills is achievable through consistent practice, critical thinking activities, and exposure to a variety of reading materials.
This article includes a list of references, related reading, or external links, but its sources remain unclear because it lacks inline citations. (July 2023) |
Inferences are steps in reasoning, moving from premises to logical consequences; etymologically, the word infer means to "carry forward". Inference is theoretically traditionally divided into deduction and induction, a distinction that in Europe dates at least to Aristotle (300s BCE). Deduction is inference deriving logical conclusions from premises known or assumed to be true, with the laws of valid inference being studied in logic. Induction is inference from particular evidence to a universal conclusion. A third type of inference is sometimes distinguished, notably by Charles Sanders Peirce, contradistinguishing abduction from induction.
Various fields study how inference is done in practice. Human inference (i.e. how humans draw conclusions) is traditionally studied within the fields of logic, argumentation studies, and cognitive psychology; artificial intelligence researchers develop automated inference systems to emulate human inference. Statistical inference uses mathematics to draw conclusions in the presence of uncertainty. This generalizes deterministic reasoning, with the absence of uncertainty as a special case. Statistical inference uses quantitative or qualitative (categorical) data which may be subject to random variations.
English 
Español 
Français