Description: Observation bias is a phenomenon that occurs when the data collected in a study or experiment is not representative of the target population. This bias can arise due to various reasons, such as inadequate sample selection, lack of randomness in data collection, or the influence of external factors affecting observation. As a result, the conclusions drawn from this data may be incorrect or misleading, compromising the validity of statistical analyses and inferences made. In the field of data science and statistics, observation bias is a critical aspect to consider, as it can lead to poorly founded decisions and the implementation of ineffective policies. It is essential for researchers and analysts to be aware of this bias and take measures to mitigate it, ensuring that the data is as representative as possible of the population being studied. Identifying and correcting observation bias is fundamental to ensuring the integrity of results and confidence in the conclusions drawn from the analyzed data.
