anòmales

Anòmales is a term in Catalan used to refer to anomalies or irregularities—entities or observations that lie outside the expected pattern or norm. The word derives from the Greek anōmalos through Latin, and it is related to the adjective anòmal, meaning irregular or deviant.

In practice, anòmales appear across many disciplines. In science and engineering they indicate deviations from a model, a measurement, or a designed specification, and may signal errors, sensor faults, or the discovery of new phenomena. In statistics and data analysis they are often called outliers: observations that differ markedly from the rest of the data. In medicine and biology they include congenital or structural irregularities. In climatology and geography they describe deviations from long-term averages, such as temperature or precipitation anomalies.

Detecting anòmales usually involves comparing observations to expectations, using rule-based thresholds, statistical tests, or machine-learning–based anomaly detection. The interpretation of anòmales requires assessing whether deviations are meaningful signals, noise, or artifacts of data collection. Handling them appropriately is important for data quality, model performance, and decision-making.

Challenges include distinguishing true anomalies from random variation, dealing with imbalanced data, and avoiding bias in detection methods. Documentation and domain context are essential to understand the implications of identified anòmales.

See also: anomaly, outlier, anomaly detection, irregularity.