Cherry Picking

The logical fallacy of selecting only the data, examples, or evidence that supports your conclusion while ignoring the much larger body of evidence that contradicts it. Different from confirmation bias (unconscious attention to confirming evidence), cherry picking is often deliberate: presenting a curated subset of reality as if it were representative. A company reporting only favorable metrics, a researcher citing only supporting studies, or a politician quoting only convenient statistics—all are cherry picking. The fallacy is invisible to audiences who don't know what's been excluded.

When to use it

When evidence presented is suspiciously one-sided; when evaluating marketing claims, research summaries, or strategic proposals; when building your own arguments and need to ensure intellectual honesty; when data analysis could be skewed by selection of time periods, metrics, or comparison groups.

How it can help

When evaluating any evidence-based argument, ask: is this the full picture or a selected subset? The diagnostic: does the presenter acknowledge ANY evidence against their conclusion? If the evidence all points one way, either the conclusion is obvious (unlikely for interesting questions) or the evidence has been cherry picked. In your own work: the antidote is steelmanning the counterevidence—presenting the strongest case against your conclusion before arguing for it. This builds credibility and ensures your analysis survives contact with the full evidence base.