Bias and variance are fundamental concepts in data science that are essential for understanding the performance of data models, yet they can be challenging even for data scientists to grasp thoroughly. This video aims to clarify these concepts by providing definitions based on logical reasoning and exploring the consequences of high bias and high variance, which lead to underfitting and overfitting, respectively. It also discusses strategies to address these issues and examines the bias-variance trade-off, noting that it is no longer as significant a concern as it once was.