The measure of how well a statistical model fits the data is known as?

The measure of how well a statistical model fits the data is known as goodness of fit. This concept assesses the alignment between the observed data and the values predicted by the model. A model that demonstrates a high goodness of fit indicates that it accurately represents the data, capturing the underlying patterns and trends effectively. Common statistical tests and measures used to evaluate goodness of fit include the Chi-squared test, R-squared value, and residual analysis, among others.

The other options—standard deviation, mean, and variance—are fundamental statistical concepts used to describe data distributions, but they do not specifically evaluate how well a model conforms to actual data. Standard deviation measures the dispersion of data points around the mean, variance quantifies the extent of variability between data points, and the mean provides the average value of a dataset. These metrics are important for understanding the characteristics of the data but do not directly pertain to evaluating the fit of a statistical model to that data.