Dangers of Extrapolation

Definition

Extrapolation refers to making predictions about values that lie outside the observed range of data in a dataset.
It assumes that the observed pattern within the dataset will continue in the same way beyond its range.

Risks and Dangers

Inaccuracy: Extrapolating can lead to highly inaccurate results. The pattern or relationship between variables evident within the dataset may not apply outside its range. This can result in significant prediction errors.
Misleading Conclusions: Based on flawed extrapolations, one can draw misleading conclusions about the behaviour or attributes of the dataset. These erroneous conclusions can profoundly impact decision-making processes.
The further away the predicted value is from the collected data, the less reliable the projection becomes. This is due to increasing levels of uncertainty.

Variables and Phenomena

Real-world phenomena are often influenced by numerous variables. Extrapolation does not consider external variables or changes in conditions outside the observed dataset.
In many scenarios, the relationship between variables is not linear, and hence, extrapolation based on a linear model may not capture these non-linearities. This can further increase the chances of inaccurate predictions.

Practical Use and Awareness

While extrapolation can be conversely productive when conducted with explicit understanding of the associated risks, it should be generally performed with extreme caution.
Always consider the context of the dataset. If there is reasonable theoretical or empirical evidence that the observed pattern will continue beyond the data range, the risks associated with extrapolation may be mitigated.

Remember, always validate your extrapolations against actual data whenever possible. Understanding the limitations and dangers of extrapolation will allow you to better interpret results obtained from bivariate distribution analysis, leading to more accurate and reliable conclusions.