Since refinement depends on finding the best fit between a calculated and experimental pattern, it is important to have a numerical figure of merit quantifying the quality of the fit. Below are the figures of merit generally used to characterize the quality of a refinement. They provide insight to how well the model fits the observed data.
Profile residual (reliability factor):
Weighted profile residual:
Bragg residual:
Expected profile residual:
Goodness of fit:
It is worth mentioning that all but one () figure of merit include a contribution from the background. There are some concerns about the reliability of these figures, as well there is no threshold or accepted value which dictates what represents a good fit. The most popular and conventional figure of merit used is the goodness of fit which should approach unity given a perfect fit, though this is rarely the case. In practice, the best way to assess quality is a visual analysis of the fit by plotting the difference between the observed and calculated data plotted on the same scale.