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Discover Ludwig"spurious results" is a correct and commonly used phrase in written English.
It refers to false or misleading findings or outcomes from a study, experiment, or analysis. Example: The researchers discovered that the initial data collected was flawed, leading to spurious results that could not be replicated.
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"People will go out and get sometimes expensive tests which give out spurious results.
This will, in turn, cause spurious results in the statistical analyses.
We clean out various spurious results from large institutions that are not ISPs.
We discuss a method aimed at reducing the risk that spurious results are published.
Currently working with forward Euler integration, which may give spurious results.
One fund industry economist publicly said Professor Zitzewitz had "to torture his data" to produce "spurious" results.
She also said his "negative controls" were clear, which she agreed suggested that there were no spurious results.
Specifically, when test statistics are not independent of the selection criteria, common analyses can produce spurious results.
When this condition is not met, data analyses yield spurious results leading to futile conclusions.
When this problem is not appreciated spurious results may be accepted as correct.
A limitation of these filters is that they may yield spurious results.
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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