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* [[What is Precision?]] | * [[What is Precision?]] | ||
* [[Describing Precision]]: Units, terms, and relationships | * [[Describing Precision]]: Units, terms, and relationships | ||
| − | * [[ | + | * [[Precision Models]]: Statistical approaches for efficient estimation and inference of precision |
* [[Prior Art]]: Reviews of past efforts to address this question | * [[Prior Art]]: Reviews of past efforts to address this question | ||
* [[FAQ]] | * [[FAQ]] | ||
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When testing a gun, shooter, and/or ammunition the most popular measure is [[Range Statistics#Extreme Spread|Extreme Spread]] or "size" of a sample target group. However, as we will illustrate throughout this site, Extreme Spread is not only a statistically inefficient measure but also one frequently and easily abused. | When testing a gun, shooter, and/or ammunition the most popular measure is [[Range Statistics#Extreme Spread|Extreme Spread]] or "size" of a sample target group. However, as we will illustrate throughout this site, Extreme Spread is not only a statistically inefficient measure but also one frequently and easily abused. | ||
| − | We advocate for [[Describing_Precision#Invariant_Measures|size-invariant measures]] like [[Circular Error Probable]] or Mean Radius. The expected value of these does not change with the number of shots on target. Instead [[ | + | We advocate for [[Describing_Precision#Invariant_Measures|size-invariant measures]] like [[Circular Error Probable]] or Mean Radius. The expected value of these does not change with the number of shots on target. Instead [[Precision_Models#How_large_a_sample_do_we_need.3F|taking more shots serves only to reduce the statistical error in our measurement]]. |
Since shooting large samples on a single target risks developing [[ragged holes]] where data points are lost, there are three recommended approaches to efficiently build data sets for estimating precision. These are explained in more detail in [[Measuring Tools]]: | Since shooting large samples on a single target risks developing [[ragged holes]] where data points are lost, there are three recommended approaches to efficiently build data sets for estimating precision. These are explained in more detail in [[Measuring Tools]]: | ||
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Furthermore, if we assume that the inherent shot dispersion is free of directional bias then we can use [[Closed Form Precision|closed-form expressions]] to calculate and analyze precision. | Furthermore, if we assume that the inherent shot dispersion is free of directional bias then we can use [[Closed Form Precision|closed-form expressions]] to calculate and analyze precision. | ||
| − | [[:Category:Examples|Examples]] of the application of these [[ | + | [[:Category:Examples|Examples]] of the application of these [[Precision Models|methods]] and [[Measuring Tools|tools]] include: |
* Determining the likelihood of a hit on a particular target by a zeroed shooting system. | * Determining the likelihood of a hit on a particular target by a zeroed shooting system. | ||
* Comparing the inherent precision of different shooting systems. | * Comparing the inherent precision of different shooting systems. | ||
* Determining which ammunition shoots better in a particular gun. | * Determining which ammunition shoots better in a particular gun. | ||
Revision as of 16:04, 22 April 2015
This site explains and demonstrates statistics for analyzing the precision of guns.
In particular:
- What is Precision?
- Describing Precision: Units, terms, and relationships
- Precision Models: Statistical approaches for efficient estimation and inference of precision
- Prior Art: Reviews of past efforts to address this question
- FAQ
Synopsis
When testing a gun, shooter, and/or ammunition the most popular measure is Extreme Spread or "size" of a sample target group. However, as we will illustrate throughout this site, Extreme Spread is not only a statistically inefficient measure but also one frequently and easily abused.
We advocate for size-invariant measures like Circular Error Probable or Mean Radius. The expected value of these does not change with the number of shots on target. Instead taking more shots serves only to reduce the statistical error in our measurement.
Since shooting large samples on a single target risks developing ragged holes where data points are lost, there are three recommended approaches to efficiently build data sets for estimating precision. These are explained in more detail in Measuring Tools:
- Use Danielson's 2-shot method: Fire two shots per target and use calipers to measure their distance from each other. This provides two samples per target with radius r = spread / 2.
- Fire one shot per target. Manually overlay them or use software like OnTarget Target Data System to automatically aggregate them into a single sample group.
- Use a logging electronic target. (Not yet widely available.)
Furthermore, if we assume that the inherent shot dispersion is free of directional bias then we can use closed-form expressions to calculate and analyze precision.
Examples of the application of these methods and tools include:
- Determining the likelihood of a hit on a particular target by a zeroed shooting system.
- Comparing the inherent precision of different shooting systems.
- Determining which ammunition shoots better in a particular gun.
