# A Systematic Procedure for Assigning Uncertainties to Data Evaluations

## Abstract

In this report, an algorithm that automatically constructs an uncertainty band around any evaluation curve is described. Given an evaluation curve and a corresponding set of experimental data points with x and y error bars, the algorithm expands a symmetric region around the evaluation curve until 68.3% of a set of points, randomly sampled from the experimental data, fall within the region. For a given evaluation curve, the region expanded in this way represents, by definition, a one-standard-deviation interval about the evaluation that accounts for the experimental data. The algorithm is tested against several benchmarks, and is shown to be well-behaved, even when there are large gaps in the available experimental data. The performance of the algorithm is assessed quantitatively using the tools of statistical-inference theory.

- Authors:

- Publication Date:

- Research Org.:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

- Sponsoring Org.:
- USDOE

- OSTI Identifier:
- 902296

- Report Number(s):
- UCRL-TR-228283

TRN: US0702922

- DOE Contract Number:
- W-7405-ENG-48

- Resource Type:
- Technical Report

- Country of Publication:
- United States

- Language:
- English

- Subject:
- 73 NUCLEAR PHYSICS AND RADIATION PHYSICS; ALGORITHMS; BENCHMARKS; EVALUATION; PERFORMANCE

### Citation Formats

```
Younes, W.
```*A Systematic Procedure for Assigning Uncertainties to Data Evaluations*. United States: N. p., 2007.
Web. doi:10.2172/902296.

```
Younes, W.
```*A Systematic Procedure for Assigning Uncertainties to Data Evaluations*. United States. doi:10.2172/902296.

```
Younes, W. Tue .
"A Systematic Procedure for Assigning Uncertainties to Data Evaluations". United States.
doi:10.2172/902296. https://www.osti.gov/servlets/purl/902296.
```

```
@article{osti_902296,
```

title = {A Systematic Procedure for Assigning Uncertainties to Data Evaluations},

author = {Younes, W},

abstractNote = {In this report, an algorithm that automatically constructs an uncertainty band around any evaluation curve is described. Given an evaluation curve and a corresponding set of experimental data points with x and y error bars, the algorithm expands a symmetric region around the evaluation curve until 68.3% of a set of points, randomly sampled from the experimental data, fall within the region. For a given evaluation curve, the region expanded in this way represents, by definition, a one-standard-deviation interval about the evaluation that accounts for the experimental data. The algorithm is tested against several benchmarks, and is shown to be well-behaved, even when there are large gaps in the available experimental data. The performance of the algorithm is assessed quantitatively using the tools of statistical-inference theory.},

doi = {10.2172/902296},

journal = {},

number = ,

volume = ,

place = {United States},

year = {Tue Feb 20 00:00:00 EST 2007},

month = {Tue Feb 20 00:00:00 EST 2007}

}