Trying to make correlations appear among data where there is none should be considered intellectual dishonesty but as is the case, this has been happening as mainstream practice in the highest levels of academia. Only recently are statisticians cracking down on these misconducts.
Playing with data to meet the significance thresholds required for publication — known as p-hacking — is an actual thing in academia. In fact, for decades, it’s been mainstream practice, partly due to researchers’ lack of understanding of common statistical methods.
But in recent years, many academics have gone through a methodological awakening, taking a second look at their own work, in part due to heightened concern and attention over p-hacking.
Perhaps the most high-profile recent case of mining and massaging of data was that of food scientist Brian Wansink, who eventually resigned from Cornell University after being found to have committed scientific misconduct.
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It is like giving someone a recipe vs. teaching them cooking techniques and ingredients. Things work fine if you have exactly what you need, but go awry if you need substitutes. Sometimes novices don't even realize they made a substitute. And there are the few special ones that will not realize they should sometimes peel a shrimp or remove the seeds from hot pepper, and then say the recipe is broken because that was not spelled out.