Groundhog Day Punxsutawney Phil Accuracy Statistics
Observed accuracy of actual predefined Groundhog-Dayprediction/event pairs within caller independently verified location, period, lead-time and outcome definition. Unknown observations retained; no fabricated Phil accuracy percentage or climatological skill.
Description
Prediction accuracy is the proportion of correctly predicted outcomes in a defined set of known pairs.
Prediction accuracy is the proportion of correctly predicted outcomes in a defined set of known pairs.1
Groundhog forecasts need independently recorded predictions, outcomes and a consistent weather definition before accuracy can be compared. Use this tool to calculate groundhog Day Punxsutawney Phil Accuracy Statistics from the supplied measurements and assumptions.
When to use Groundhog Day Punxsutawney Phil Accuracy Statistics
Use this tool to calculate groundhog Day Punxsutawney Phil Accuracy Statistics from the supplied measurements and assumptions. Identify the community, narrative version, source edition and interpretation context; prepare any annotations independently.
How to use Groundhog Day Punxsutawney Phil Accuracy Statistics and understand its results
- Identify the community, narrative version, source edition and interpretation context; prepare any annotations independently.
- Enter independentlyVerifiedActualEventFlags, recordedGroundhogPredictionFlags, pairedObservationKnownFlags, using the field meanings and rules below, then run the tool.
- Read result.correctKnownCount, result.incorrectKnownCount, result.unknownCount, result.observedKnownAccuracy. Interpret the fields using their units, signs and evidence conventions below.
- If input validation fails, correct the reported field and run again. Before applying the result, check reference scope, model assumptions and any missing evidence or placeholder values.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| independentlyVerifiedActualEventFlags | Independently verified actual event flags; binary 0/1 under the definitions below; caller assertion or measurement requiring independent evidence | list of number | Required |
| recordedGroundhogPredictionFlags | Recorded groundhog prediction flags; binary 0/1 under the definitions below | list of number | Required |
| pairedObservationKnownFlags | Paired observation known flags; binary 1=known, 0=unknown, in the order described below | list of number | Required |
Enter nonempty aligned binary forecasts, independently determined outcomes and paired-known flags. Require at least one known pair; count equality only there and return correct, incorrect and unknown counts. Accuracy is correct/known as a fraction, despite the title's statistics wording. No forecast is generated or weather outcome inferred.
accuracy=correctKnownCount/(correctKnownCount+incorrectKnownCount) Use the exact units, boundary rules, ordering and missing-evidence conventions above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Structured calculation or selected reference, with fields listed below | object |
| result.correctKnownCount | Correct Known Count; follow the units, sign, ordering and evidence convention stated above | number |
| result.incorrectKnownCount | Incorrect Known Count; follow the units, sign, ordering and evidence convention stated above | number |
| result.unknownCount | Unknown Count; follow the units, sign, ordering and evidence convention stated above | number |
| result.observedKnownAccuracy | Correct known pairs divided by all known pairs; a fraction, multiply by 100 for percent | number |
{
"input": {
"independentlyVerifiedActualEventFlags": [
1,
0,
1,
0
],
"recordedGroundhogPredictionFlags": [
1,
1,
1,
0
],
"pairedObservationKnownFlags": [
1,
1,
0,
1
]
},
"output": {
"result": {
"correctKnownCount": 2,
"incorrectKnownCount": 1,
"unknownCount": 1,
"observedKnownAccuracy": 0.6666666666666666
}
}
}Limitations and assumptions
Groundhog forecasts need independently recorded predictions, outcomes and a consistent weather definition before accuracy can be compared. Interpretation depends on the units, sampling scope and exact model described above.
All listed fields require explicit values. Unexpected fields, incorrect types, nonfinite numbers and sparse evidence arrays fail validation; the specific bounds and alignment requirements are stated above. Extremely large inputs or outputs can also be rejected. An input error is distinct from a successful result reporting false, no matches or missing evidence.
Alternative or Complementary methods
Use Cricket Chirps TemperatureDolbear's Law Formula Calculator for this related task: Dolbear-type relations associate cricket chirp rate with a conditional temperature estimate.
Use Birds On a Wire Storm Pressure Sense Claim Checker when its separate inputs and assumptions fit the question: Birds resting on wires can be discussed through observed behavior and circulated weather claims.
References
Similar or alternative tools
- Cricket Chirps TemperatureDolbear's Law Formula Calculator
Dolbear-type relations associate cricket chirp rate with a conditional temperature estimate.
- Birds On a Wire Storm Pressure Sense Claim Checker
Birds resting on wires can be discussed through observed behavior and circulated weather claims.