When extrapolating data, why is it important to consider the known values?

Considering known values when extrapolating data is crucial for ensuring accuracy in the estimated range. Known values serve as the foundation upon which predictions are made. They provide key insights into the trends and patterns present within the data set. When these known values are analyzed, they help form a clearer understanding of how variables relate to each other, allowing for more informed predictions beyond the existing data.

If the extrapolation is grounded in a robust understanding of the known data, it is more likely that the estimates will reflect realistic scenarios rather than deviating wildly from what has been observed. Ignoring known values can lead to unreliable forecasts that do not accurately represent the situation being modeled. Therefore, utilizing known values effectively enhances the reliability of extrapolated conclusions.