Avoiding Two Subtle Logical Pitfalls
False Disjunction Bias and Cherry-Picking
Sungchul Ji, Ph.D. (with ChatGPT assistance)
Emeritus Professor of Theoretical Cell Biology
Ernest Mario School of Pharmacy,
Rutgers University, Piscataway, NJ
1. Introduction
In science, how we frame questions and interpret data can be as critical as the data itself. Two common logical pitfalls—False Disjunction Bias (FDB) and the Cherry-Picking Error—often creep into reasoning, subtly distorting both theory and evidence.
While cherry-picking is widely recognized, False Disjunction Bias is less well-known but equally damaging. This post explains both errors, shows how they relate, and offers guidance to scientists for avoiding them.
2. What Is False Disjunction Bias?
False Disjunction Bias (FDB) arises when two concepts AAA and BBB are falsely treated as mutually exclusive, even though both may be complementary aspects of a larger whole CCC.
Logical Form:
“Either AAA or BBB”
instead of
“AAA and BBB are both aspects of CCC.”Examples:
Saying “Organisms are not machines” is an FDB if we define:
Machine = Physical Laws (PL)
Organism = PL + Biological Laws (BL).
Organisms are more than machines, but they are not less than machines.Claiming “Sound is not a wave but a bubble” is an FDB, since sound bubbles are simply 3D manifestations of sound waves.
FDB often arises when a triadic relationship (A–B–C) is mistakenly collapsed into a false binary (A vs. B).
3. What Is Cherry-Picking?
Cherry-picking is the selective use of evidence that supports one’s claim while ignoring contradictory evidence.
Example:
Citing only studies that support a drug’s effectiveness while dismissing those that show no effect.
4. How Are They Related?
FDB distorts the conceptual structure (how A and B relate to C).
Cherry-picking distorts the evidence base (which data are considered).
Together, they reinforce biased narratives:
Frame the issue falsely as A vs. B (FDB),
Then cite only evidence for A (cherry-picking),
Concluding incorrectly: “Only A is true.”
5. Examples Across Disciplines
6. How to Avoid These Errors
Always ask: Is there a larger whole (C) that unites A and B?
Check complementarity: Opposing views may be two sides of the same coin.
Audit your evidence: Are you ignoring contradictory data (cherry-picking)?
Use triadic thinking: Move beyond “either/or” to “both/and” where appropriate.
7. Conclusion
Both FDB and cherry-picking distort the scientific process—not by falsifying data, but by framing reality incorrectly. Recognizing these pitfalls can make our theories more accurate and our science more holistic.