Data bias refers to a systematic error in data collection, analysis, or interpretation that results in inaccurate or misleading conclusions. This bias can be introduced at various stages of the research process, including the selection of study participants, the design of surveys or experiments, the handling and processing of data, and the presentation of results. Data bias can arise from a variety of sources, such as sample selection bias, measurement bias, or researcher bias. Sample selection bias occurs when the chosen sample is not representative of the population being studied, leading to results that may not be generalizable. Measurement bias occurs when the methods used to collect or measure data are flawed, resulting in inaccurate or incomplete information. Researcher bias refers to the influence of the researcher's own beliefs, preferences, or assumptions on the interpretation of data. Addressing data bias is crucial in research to ensure the validity and reliability of findings. Researchers must be vigilant in identifying and mitigating any sources of bias in their study design, data collection, and analysis methods. By doing so, they can minimize the risk of drawing incorrect or misleading conclusions from their research.