Interpolation / Extrapolation Fallacy
Assumes trends within observed data automatically hold between or beyond the data points without justification.
- •Definition: Assumes trends within observed data automatically hold between or beyond the data points without justification.
- •Impact: Interpolation / Extrapolation Fallacy distorts reasoning by Patterns can change outside observed ranges. Without evidence, extending the trend is speculative and can ignore constraints or curve changes.
- •Identify: Look for patterns like Observe a trend in a limited range of data.
What is the Interpolation / Extrapolation Fallacy?
Fitting a line or noticing a pattern does not guarantee it continues smoothly in unobserved ranges. Interpolating between scarce points or extrapolating far beyond the data can misrepresent reality when relationships are non-linear or constrained.
People lean on this pattern because Simple straight-line thinking is easy and persuasive; it avoids dealing with uncertainty or complex models.
- 1Observe a trend in a limited range of data.
- 2Assume the same relationship holds between or beyond observed points.
- 3Draw conclusions without testing the new range or considering limits.
Why the Interpolation / Extrapolation Fallacy fallacy matters
This fallacy distorts reasoning by Patterns can change outside observed ranges. Without evidence, extending the trend is speculative and can ignore constraints or curve changes.. It often shows up in contexts like Forecasting, Business projections, Health claims, where quick takes and ambiguity can hide weak arguments.
Examples of Interpolation / Extrapolation Fallacy in Everyday Life
A health metric improves in a small trial; marketers project identical improvement to whole populations over years without long-term or larger studies.
Why it is fallacious
Patterns can change outside observed ranges. Without evidence, extending the trend is speculative and can ignore constraints or curve changes.
Why people use it
Simple straight-line thinking is easy and persuasive; it avoids dealing with uncertainty or complex models.
Recognition
- Linear projections beyond measured data.
- Confidence in predictions without testing new ranges.
- Omission of uncertainty or potential curve changes.
Response
- Ask for data or models validated in the new range.
- Highlight possible non-linearities or limits.
- Request uncertainty bounds instead of single-line projections.
- “Interpolation / Extrapolation Fallacy” style claim: Assumes trends within observed data automatically hold between or beyond the data points without justification.
- Watch for phrasing that skips evidence, e.g. "Assumes trends within observed data automatically hold between or beyond the data points without justification"
- Pattern hint: Observe a trend in a limited range of data.
Ask for data or models validated in the new range.
Interpolation / Extrapolation Fallacy is often mistaken for Regression Fallacy, but the patterns differ. Compare the steps above to see why this fallacy misleads in its own way.
Close variations that are easy to confuse with Interpolation / Extrapolation Fallacy.
Frequently Asked Questions
Interpolation / Extrapolation Fallacy signals a weak reasoning pattern. Even if the conclusion is true, the path to it is unreliable and should be rebuilt with sound support.
Interpolation / Extrapolation Fallacy follows the pattern listed here, while Regression Fallacy fails in a different way. Looking at the pattern helps choose the right diagnosis.
You will find it in everyday debates, opinion columns, marketing claims, and quick social posts—anywhere speed or emotion encourages shortcuts.
It can feel persuasive, but it remains logically weak. A careful version should replace the fallacious step with evidence or valid structure.