Laccuratezza

Laccuratezza (often written l'accuratezza in Italian) denotes the quality of being accurate: the closeness of a value, measurement, or representation to the true value or a defined standard. In usage, l'accuratezza is a central aspect of data quality, measurement, and communication, and it is commonly contrasted with related concepts such as precision and bias.

Conceptually, accuracy is assessed by comparing a result to a reference or truth. In metrology, it describes how close a measurement is to the true quantity, combining both systematic error (bias) and random error (variability). In information and data contexts, accuracy refers to how faithfully data reflect the real world or a ground truth.

Applications span diverse fields. In experimental science, accuracy is pursued through calibration against standards and careful methodology. In data science and machine learning, accuracy often means the proportion of correct predictions relative to ground truth. In journalism, translation, and scientific reporting, accuracy concerns the faithful conveyance of facts, meanings, and measurements.

Improving accuracy involves several practices: calibrating instruments against known references, using higher-quality standards, expanding and diversifying data, performing validation and cross-checks, and conducting error analysis to identify and reduce sources of bias and noise. Challenges to accuracy include measurement uncertainty, incomplete information, model bias, and evolving reference standards.

See also: precision, bias, uncertainty, verification, validation.