That's exactly right !Beer-monster said:
Interpolation is the process of estimating data points within an existing set of known data points. It is used to fill in gaps or missing values in the data. Extrapolation, on the other hand, is the process of estimating data points outside of the existing data set. It is used to make predictions or projections beyond the known data.
Interpolation is generally considered to be more accurate than extrapolation. This is because interpolation is based on existing data points and makes use of known relationships between those points. Extrapolation, on the other hand, involves making assumptions and projecting beyond the known data, which can lead to less accurate results.
Interpolation should be used when there are gaps or missing values in a data set and the goal is to fill in these gaps with estimated values. It is useful for creating a smoother, more complete data set for further analysis or visualization.
Extrapolation should be used when there is a need to make predictions or projections beyond the known data set. It can be useful for forecasting future trends or making predictions based on current data.
One potential risk of extrapolation is that the data may not follow the same pattern or relationship outside of the known data set. This can lead to inaccurate predictions or projections. Additionally, extrapolation relies on assumptions and can be affected by outliers or errors in the data, which can also lead to inaccurate results.