Amara Law Short Run Overestimate Rule Lookup
Quantify how much a short-term prediction exceeds a later outcome and report the prediction-to-outcome ratio.
Description
A prediction-versus-outcome comparison quantifies overstatement against a positive observed baseline.
A prediction-versus-outcome comparison quantifies overstatement against a positive observed baseline. A negative difference represents understatement.1
The returned values include Prediction overestimated, Overstatement percent, Prediction-to-outcome ratio. The How section below explains the calculation, input rules and interpretation.
When to use Amara Law Short Run Overestimate Rule Lookup
Use Amara Law Short Run Overestimate Rule Lookup when you need to evaluate the supplied records under the calculation and evidence rules below. This is a numerical comparison, not proof of a universal Amara law or an independent long-term forecast.
Define the question, evidence and scenario assumptions first. Supply the chosen score scales, time horizons or reference selection explicitly. Interpret generated text as a discussion template and screen labels as the stated rules.
How to use Amara Law Short Run Overestimate Rule Lookup and understand its results
- Define the question, evidence and scenario assumptions first. Supply the chosen score scales, time horizons or reference selection explicitly. Interpret generated text as a discussion template and screen labels as the stated rules.
- Enter longTermOutcome, shortTermPrediction using the field table and calculation rules, then run the tool.
- Read overestimated, overstatementPercent, predictionToOutcomeRatio. The explanation below defines the units, signs, ordering and any flags or placeholder values.
- If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.
| Input field | Meaning | Value type; see rules below | Requirement |
|---|---|---|---|
| longTermOutcome | Long-term outcome | number; minimum: 0 | Required |
| shortTermPrediction | Short-term prediction | number; minimum: 0 | Required |
Require finite nonnegative prediction and strictly positive finite outcome. Return finite signed percent and ratio; overestimated is percent>0, excluding equality.
percent=100×(prediction−outcome)/outcome; ratio=prediction/outcome Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| overestimated | Prediction overestimated | boolean |
| overstatementPercent | Overstatement percent (%) | number |
| predictionToOutcomeRatio | Prediction-to-outcome ratio | number; minimum: 0 |
{
"input": {
"longTermOutcome": 40,
"shortTermPrediction": 100
},
"output": {
"overestimated": true,
"overstatementPercent": 150,
"predictionToOutcomeRatio": 2.5
}
}Limitations and assumptions
This is a numerical comparison, not proof of a universal Amara law or an independent long-term forecast.
Numeric inputs, where present, must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Missing fields without a declared default, extra fields, wrong value types and violations of the calculation rules are input errors. A successfully returned false flag is an evaluated outcome; its meaning is explained above.
Alternative or Complementary methods
Use Extrapolation Horizon Limit Warning Checker: An extrapolation can assume uncertainty grows at a constant annual compound rate. Explicit thresholds provide an illustrative warning screen. For a complementary task, use Wright Law Learning Curve Cost Doubling Lookup: Learning curves express a hypothetical unit-cost reduction per doubling of cumulative production. The chosen rate is an assumption, not an observed future trend.
References
Similar or alternative tools
- Extrapolation Horizon Limit Warning Checker
An extrapolation can assume uncertainty grows at a constant annual compound rate. Explicit thresholds provide an illustrative warning screen.
- Wright Law Learning Curve Cost Doubling Lookup
Learning curves express a hypothetical unit-cost reduction per doubling of cumulative production. The chosen rate is an assumption, not an observed future trend.