Answer:
implying causality where none existed
Explanation:
In statistics, the phrase "implying causality where none existed" is the inadequacy to duly and lawfully infer a cause-and-effect relationship between two variables majorly on the groundwork of a noted association or correlation between them.
Another example of this kind of statistical error is finding out if ice cream consumption increases the tendency to commit crimes, as one may consider the correlation between ice cream consumption and crime, and 'imply causality where none existed' in the case.
