Inference is a cognitive process that involves drawing conclusions from available evidence and reasoning. It’s a fundamental component of critical thinking and problem-solving, playing a significant role in fields as diverse as scientific research, literature interpretation, and intelligence artificielle[1]. There are several types of inference, including deductive, inductive, abductive, statistical, and causal, each with its own unique approach and application. For instance, deductive inference is about deriving specific conclusions from general principles, while inductive inference forms general conclusions from specific observations. On the other hand, abductive inference is about making educated guesses based on available evidence, while statistical and causal inferences involve interpreting data to draw conclusions about a population or to determine cause-and-effect relationships. However, biases, preconceptions, and misinterpretations can influence the accuracy of inferences. Despite these challenges, inference remains an essential skill that can be improved through practice, critical thinking exercises, and engaging in diverse reading materials.
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Inferences are steps in reasoning, moving from premises à logical consequences; etymologically, the word infer means to "carry forward". Inference is theoretically traditionally divided into deduction et 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; intelligence artificielle 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.
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