What is the process of making predictions about unknown data points based on an existing model called?

The process of making predictions about unknown data points based on an existing model is referred to as extrapolation. This involves using established relationships or patterns within the data to make informed estimates about values that lie outside the range of the data used to create the model.

Extrapolation extends the model’s applicability beyond the known data, enabling analysts to predict outcomes or trends that have not yet been observed. This is particularly useful in various fields such as economics, environmental science, and engineering, where future predictions can influence decision-making.

In contrast, the other terms refer to different concepts. Validity pertains to the accuracy and reliability of the model itself, model choice involves selecting which model will be used for analysis, and sample size refers to the number of observations or data points collected in a study, none of which specifically address the act of predicting unknown data points based on an existing model.