Pavel,

Please correct if wrong, but I thought most refinement programs used the 
weights e.g. sig(I/F) with I/F so would not really have a hard cut off anyway? 
You’re just making the stats worse but the model should stay ~ the same (unless 
you have outliers in there)

Clearly there will be a point where the model stops improving, which is the 
“true” limit…

Cheers Graeme



On 5 Jul 2019, at 06:49, Pavel Afonine 
<pafon...@gmail.com<mailto:pafon...@gmail.com>> wrote:

Hi Sam Tang,

Sorry for a naive question. Is there any circumstances where one may wish to 
refine to a lower resolution? For example if one has a dataset processed to 2 
A, is there any good reasons for he/she to refine to only, say 2.5 A?

yes, certainly. For example, when information content in the data can justify 
it.. Randy Read can comment on this more! Also instead of a hard cutoff using a 
smooth weight based attenuation may be even better. AFAIK, no refinement 
program can do this smartly currently.
Pavel

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