Data Intuition

Pattern recognition, context, human behavior

Data intuition is the gut feeling developed from living inside dashboards long enough to sense when something is off.

A founder with data intuition does not just read numbers. They ask what human behavior created them and whether that behavior can be repeated.

Rule: averages lie, behavior tells the truth.

Section 1|

Defining the core pillars

Pattern recognition: seeing shapes in data, not isolated numbers. Changes in quality matter more than changes in volume.

Contextual interpretation: understanding where data comes from and what incentives produced it.

The sniff test: an internal alarm that flags numbers that look too good, too sudden, or too clean.

Section 2|

Base rate thinking: small sample sizes make impressive percentages meaningless.

Simpson’s paradox: trends can reverse when segments are combined, always drill down.

Vanity vs actionable metrics: growth that cannot change decisions is noise, not insight.

Section 3|

Write your expectations before checking dashboards, then study the gap between belief and reality.

Break averages into cohorts, channels, and behaviors to reveal hidden truths.

Charts expose anomalies faster than tables ever will.

Study common statistical tricks so you do not fool yourself with optimistic reporting.