Homoscedasticity refers to a situation in statistical modeling where the variance of the errors or residuals is constant across all levels of the independent variable(s). This means that the spread or dispersion of the data points remains uniform, regardless of the value of the predictor. Homoscedasticity is an important assumption in regression analysis, as it ensures that the model's predictions are reliable and valid. When the assumption of homoscedasticity is violated, the data is said to exhibit heteroscedasticity, which can lead to inefficient estimates and affect the statistical tests' validity. Detecting homoscedasticity can be done through visual methods, such as scatter plots, or statistical tests, ensuring that the model's assumptions are met for accurate analysis.