Data is encoded information.
That’s a very clean mental model. Information exists first; data is how we represent, store, transmit, and analyze it. And because encoding is never perfectly neutral, what gets measured, how it gets measured, and what gets left out matter.
Avoid vague words like “usual,” “typical,” or “normal.”
Because those words often hide an undefined statistical claim. “Typical” could mean mean, median, mode, majority, expected range, or just someone’s intuition.
Know your audience: “feature,” “variable,” and “predictor” can describe similar things, but different fields speak different dialects. Good data people translate.
Observational vs. experimental data: observing what happens is different from deliberately changing something and measuring the effect. That distinction matters heavily for causal claims.
Randomized A/B tests: randomization tries to balance confounding factors, making comparisons much more credible.