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This data is not provided by agents.
If the data is not current and completely reliable, we will fail.
With this approach, we will limit our analysis to the studies which have provided data on the selected effect modifiers, which might introduce bias if data are not missing completely at random.
Our missing data were not missing completely at random.
These methods have been shown to be biased if data are not missing completely at random.
This may introduce bias if data are not missing completely at random.
Little MCAR test [ 51] indicated that the data were not missing completely at random.
In fact, our data are not completely consistent with this prediction.
Results between food monitoring and epidemiological data were not completely consistent.
The pilot is not completely finished, and all data are not yet available.
Variables like these can result in data that is not completely accurate.
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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