Omitted Variable Bias occurs when a model fails to include one or more relevant variables that influence the outcome being studied. This can lead to incorrect conclusions about the relationships between the included variables. For example, if researchers are studying the effect of exercise on weight loss but omit diet, the results may inaccurately attribute weight loss solely to exercise. This bias can distort the estimated effects and mislead decision-making. To avoid omitted variable bias, it is essential to identify and include all relevant factors in the analysis. Properly accounting for these variables helps ensure more accurate and reliable results in research.