Statistics

What is a fit?

Best Fit Interpretation

Overconstrain/Underconstrain

The current error are given as 1sigma deviation, due to convention. So nuisance parameter are usually assigned to a Gaussian constraint with sigma=1. If the estimated error given by fitting algorithms is less than 1, we have underconstrain. If it's over 1, we have overconstrain.

The estimated errors given by the the fitting algorithm are what the algorithm defines 1sigma error. So if you have underconstrain, for example 0.9, the algorithm thought your error should be 0.9 * variation_1sigma, so you have overestimated your error

Technical procedure of minimization

Examples of metrics

-- RongkunWang - 2017-09-04