Biasstøy

Biasstøy, also known as bias noise or bias distortion, refers to unwanted variations or errors that arise in data due to a systematic inclination or prejudice in the data collection, analysis, or interpretation process. This type of noise is not random but rather reflects a consistent deviation from the true value or a lack of objectivity.

Biasstøy can manifest in various forms. In surveys or research, it might appear as sampling bias, where the chosen sample does not accurately represent the target population, or response bias, where participants provide inaccurate or misleading answers. In image processing, biasstøy could be introduced by sensor imperfections or calibration errors that consistently affect pixel values. In machine learning, it can lead to models that perform poorly on unseen data because they have learned patterns specific to the biased training set.

Identifying and mitigating biasstøy is crucial for ensuring the reliability and validity of findings. This often involves careful study design, diverse data sources, rigorous validation techniques, and awareness of potential sources of prejudice throughout the research or data handling lifecycle. Understanding the nature and origin of biasstøy helps in developing strategies to minimize its impact and achieve more accurate and equitable outcomes.