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True, the data does not confirm a protective effect of light drinking.
If it were true, the data would be the reverse.
This method checks the flag and, if it is true, the data is returned to application.
When this is not true, the data are characterized as being either over-dispersed or under-dispersed.
It seems likely that health organisations with stronger safety cultures are more sensitised to this issue and that the staff of such organisations would be more likely to respond; if this was true, the data from this study would be an over estimate of the real state of affaires.
Similar(55)
Returns true if the data source used by this projector is empty, otherwise returns true.
NORMFLAG TRUE Is the data normalized?
That may be true and the data presented here are certainly interesting in that regard.
Returns true if the data source used by this projector is empty.
The userlimit parameter is a dmmath expression that should evaluate to TRUE when the data are GOOD.
This is especially true if the data models are created through reverse engineering from existing databases without declared foreign keys.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com