Top-down mandates from authorities have a history of being flawed, from the food pyramid to the FDA's stance on opioids. True progress emerges not from command-and-control edicts but from a decentralized system that allows for thousands of experiments. Protecting the freedom for most to fail is what allows a few breakthrough ideas to succeed and benefit everyone.

To combat confirmation bias, withhold the final results of an experiment or analysis until the entire team agrees the methodology is sound. This prevents people from subconsciously accepting expected outcomes while overly scrutinizing unexpected ones, leading to more objective conclusions.

A key feature making economics research robust is its structure. Authors not only present their thesis and evidence but also anticipate and systematically discredit competing theories for the same outcome. This intellectual honesty is a model other social sciences could adopt to improve credibility.

When a technology reaches billions of users, negative events will inevitably occur among its user base. The crucial analysis isn't just counting incidents, but determining if the technology increases the *rate* of these events compared to the general population's base rate, thus separating correlation from causation.

A study on a Chinese policy providing free coal heating north of the Huai River, but not south, created a natural experiment. This revealed that the resulting increase in particulate pollution caused residents in the north to live, on average, five years less than their southern counterparts.

The dramatic decline in childhood peanut allergies offers a clear victory for public health policy. A 2015 reversal in official guidance—from avoidance to encouraging early exposure for infants—is directly credited with a 40% overall reduction, demonstrating how evidence-based policy can rapidly change health outcomes.

A two-step analytical method to vet information: First, distinguish objective (multi-source, verifiable) facts from subjective (opinion-based) claims. Second, assess claims on a matrix of probability and source reliability. A low-reliability source making an improbable claim, like many conspiracy theories, should be considered highly unlikely.

To counteract the brain's tendency to preserve existing conclusions, Charles Darwin deliberately considered evidence that contradicted his hypotheses. He was most rigorous when he felt most confident in an idea—a powerful, counterintuitive method for maintaining objectivity and avoiding confirmation bias.

Current LLMs fail at science because they lack the ability to iterate. True scientific inquiry is a loop: form a hypothesis, conduct an experiment, analyze the result (even if incorrect), and refine. AI needs this same iterative capability with the real world to make genuine discoveries.