Interpolation is the estimation of a value within the range of observed data, while extrapolation estimates a value beyond that range using an assumed pattern or model, often represented in a table, graph, or linear relationship. The distinction includes recognizing that extrapolation is generally less reliable because the observed trend may not continue outside the data, supporting reasoned interpretation of models and predictions without extending to advanced statistical modeling.
Interpolation and extrapolation both use a pattern in observed data, but the key difference is where the estimate is made:
A cyclist records the distance traveled after different numbers of hours:
| Time (hours) | Distance (km) |
|---|---|
| 1 | 50 |
| 2 | 100 |
| 3 | 150 |
| 4 | 200 |
The data show a pattern: the cyclist travels km each hour.
The observed times range from to hours. Since is within this range, this is interpolation.
The distance at hours is km, and the distance at hours is km. Halfway between and is
So, the estimated distance after hours is
Six hours is beyond the largest observed time, which is hours. Therefore, this is extrapolation.
If the pattern continues at km per hour, then
The estimated distance after hours is
However, this estimate is less certain. The cyclist might slow down, stop, or face a hill, so the pattern from to hours may not continue.
To decide which type of estimate you are seeing, first identify the range of the observed data. An estimate within that range is interpolation; an estimate beyond it is extrapolation.
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