Multicollinearity refers to a situation in statistical modeling where two or more independent variables are highly correlated. This means that they provide redundant information about the response variable, making it difficult to determine the individual effect of each variable. As a result, the estimates of the coefficients can become unstable and unreliable. When multicollinearity is present, it can lead to inflated standard errors, which may affect hypothesis tests and confidence intervals. To detect multicollinearity, analysts often use techniques like the Variance Inflation Factor (VIF) or correlation matrices. Addressing it may involve removing or combining variables to improve model accuracy.