why is the extrapolation of data points from models beyond the time frame of the original data considered suspect?
e.g.
consider a graph a mother made of her child
the graph would continue to rise to about an age of appr. 21, then it would stay level, (actually starts to drop as you get older, as I well experience, lol)
If you were to look at the graph to the end of age 14 for example and saw no more of the graph, it would be totally false to assume it continue at that rate for, let's say age 24.
The extrapolation of data points beyond the time frame of the original data is considered suspect for several reasons. Let's discuss why:
1. Uncertainty: Extrapolation assumes that the patterns observed in the available data will continue in the future. However, this assumption is not always valid, as various factors can affect the underlying patterns, leading to different outcomes. When extrapolating, we assume that the relationships between variables remain constant, which may not hold true over an extended period. As a result, the further we move away from the original data, the higher the uncertainty in the extrapolated results.
2. Limited coverage: Extrapolation involves extending the trends observed within a specific time frame to make predictions beyond that period. However, this technique assumes that the historical data sufficiently represents all future scenarios. In reality, the original data may not capture all possible scenarios, making the predictions less reliable.
3. Shifting variables: Over time, the variables affecting the data might change, introducing new influences that were not present in the original data. These changes can invalidate the assumptions made during extrapolation, thus making the predictions less accurate.
4. Complexity: Many real-world phenomena are complex and can exhibit non-linear behavior. Extrapolation assumes a linear or predictable relationship between the variables, which might not hold true over extended periods. Non-linearities can arise that lead to unexpected outcomes, making the extrapolated results unreliable.
Given these reasons, the further we move away from the time frame of the original data, the less reliable the extrapolation becomes. To mitigate these concerns, it is often advisable to use caution when relying solely on extrapolated data and consider incorporating other analytical techniques and domain knowledge to improve the accuracy of predictions.