Definitions
- Referring to the estimation of values within a set of known data points. - Used in mathematics, statistics, and computer science to predict values between two known data points. - Describing the process of filling in missing data points within a range of known data points.
- Referring to the estimation of values beyond a set of known data points. - Used in mathematics, statistics, and science to predict values outside the range of known data points. - Describing the process of extending a trend or pattern beyond the available data points.
List of Similarities
- 1Both involve estimating values based on known data points.
- 2Both are used in mathematics, statistics, and science.
- 3Both can be used to predict future trends or patterns.
- 4Both require a set of known data points to work with.
- 5Both can be used to fill in gaps in data.
What is the difference?
- 1Scope: Interpolation estimates values within a set of known data points, while extrapolation estimates values beyond the known data points.
- 2Accuracy: Interpolation is generally more accurate than extrapolation since it is based on known data points.
- 3Risk: Extrapolation carries a higher risk of error since it involves predicting values outside the known data range.
- 4Purpose: Interpolation is used to estimate values within a range, while extrapolation is used to predict values beyond the range.
- 5Application: Interpolation is commonly used in computer graphics and image processing, while extrapolation is often used in economics and finance.
Remember this!
Interpolation and extrapolation are both methods of estimating values based on known data points. However, interpolation estimates values within a set of known data points, while extrapolation estimates values beyond the known data points. Interpolation is generally more accurate but has a limited scope, while extrapolation carries a higher risk of error but can predict values beyond the known range.
