We are wired by conversational rules (pragmatics) to assume superfluous information is important. When given irrelevant but descriptive details, we ignore the statistical base rate because we assume the details were provided for a reason, a cognitive quirk that can be exploited.

Deceivers hijack our trust in precision by attaching specific numbers (e.g., "13.5% of customers") to their claims. This gives a "patina of rigor and understanding," making us less likely to question the source or validity of the information itself, even if the number is arbitrary.

The "Batman Effect" study's choice of a superhero to test a "disruption" hypothesis introduces a glaring confound (priming heroism). This may be a deliberate strategy to create ambiguity, ensuring a stream of follow-up studies is needed to disentangle the effects, thus building a literature.

A psychology study's attempt to measure "state disinhibition" by assessing "bystander apathy" is highlighted as a convoluted and meaningless methodological leap. This shows how academic research can become detached from common sense in its pursuit of novel metrics.

Linguist Anne Kerzan reframes jargon not as a flaw, but as a specialized lexicon for a profession. It provides useful shortcuts and creates a sense of shared identity for insiders. The negative perception arises from being an outsider or when jargon is used to obscure meaning, such as with corporate euphemisms.

The public appetite for surprising, "Freakonomics-style" insights creates a powerful incentive for researchers to generate headline-grabbing findings. This pressure can lead to data manipulation and shoddy science, contributing to the replication crisis in social sciences as researchers chase fame and book deals.

People lack the attention for complex solutions. A simple, memorable soundbite, like Donald Trump's "Build a wall," will often defeat a comprehensive, nuanced plan, like Jeb Bush's book on immigration. The message with the lowest cognitive load wins, regardless of its substance.

People's conscious, stated reasons for their actions (proximate explanations) often obscure deeper, unconscious evolutionary drivers (ultimate explanations), such as the drive to reduce mating competition while appearing compassionate.

We tend to stop analyzing data once we find a conclusion that feels satisfying. This cognitive shortcut, termed "explanatory satisfaction," is often triggered by confirmation bias or a desire for a simple narrative, preventing us from reaching more accurate, nuanced insights.