Sentence examples for because divided from inspiring English sources

Exact(3)

The Labour party is hesitant in action, because divided in mind.

"Franklin urged us to, 'Let's make the choice: We stand as one because divided we fail.' Her plea is as timeless today as it was more than a decade ago.

It doesn't really matter which party started it (both of them) or whether this was avoidable (it was), because divided government has again led us to a place where the most important policy decisions are probably going to be made in secret, and then passed down to the rest of us.

Similar(55)

Perhaps because dividing movies into the only categories that really matter -- "good" and "bad" -- seems both overly facile and intellectually overwhelming, we devise more elaborate, more knowing ways of sorting them out.

Our attempts to relate haplotypes to mRNA levels were not successful because dividing individuals into groups based on haplotypes resulted in small numbers of subjects in certain haplotype groupings.

And if great isn't good enough, then you can have an infinitely good weekend by staying at home and cutting your travel time to zero (because dividing stuff by zero makes infinity).

Third, companies must conduct controlled experiments, because "divide and rule" do not work anymore to handle machines that mimic human thinking.

Even in data-limited situations, the assumption of a single variance term across diet components is reasonable when using the CLR transformation because dividing by the geometric mean places diet components on similar scales.

This difference was random, because dividing of animals to groups was random and treatment was started on the day 3 PP.

Medication was not included into statistics neither as variable due to the high diversity of drugs used nor as a factor with 2 levels because dividing these small patient groups into subgroups would have further weakened our results.

We also do not advise comparing creatinine-corrected estimates across populations because dividing by creatinine can produce false-negative associations with predictors of lean body mass, which may vary across populations or subgroups within a study (Barr et al. 2005).

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