Statistics
A discipline is a lens, not an owner — the same idea can be viewed through many.
Concepts viewed through this lens
These numbers describe the current Thinking OS knowledge slice, not the whole field.
Disciplines it bridges to
Each bridge is built by ideas the two fields share, the thinking patterns that recur across both, and a representative relationship that shows how they connect.
Statisticsconnects toMathematics
Shared concepts
Shared thinking patterns
Why this bridge exists
Combinatorics enables Probability — combinatorics enables Probability.
Explore this connection →Statisticsconnects toComputer Science
Shared concepts
Shared thinking patterns
Why this bridge exists
Error detection and correction suppresses Noise — error detection and correction relates to Noise.
Explore this connection →Statisticsconnects toMachine Learning
Shared thinking patterns
Why this bridge exists
Supervised learning enables Generalization — supervised learning enables Generalization.
Explore this connection →Statisticsconnects toBiology
Shared concepts
Shared thinking patterns
Why this bridge exists
Experiment suppresses Confounding variable — A controlled experiment holds confounders fixed.
Explore this connection →Statisticsconnects toData Science
Shared concepts
Shared thinking patterns
Why this bridge exists
Supervised learning enables Generalization — supervised learning enables Generalization.
Explore this connection →Statisticsconnects toPhilosophy of Science
Shared concepts
Shared thinking patterns
Why this bridge exists
Experiment suppresses Confounding variable — A controlled experiment holds confounders fixed.
Explore this connection →Statisticsconnects toProbability
Shared concepts
Shared thinking patterns
Why this bridge exists
Expectation depends on Probability distribution — expectation depends on probability distribution.
Explore this connection →Statisticsconnects toEconomics
Shared concepts
Shared thinking patterns
Why this bridge exists
Diversification suppresses Risk — diversification relates to Risk.
Explore this connection →
Field shape — representation health
72/100 overall · 34 concepts
The eight dimensions measure Thinking OS coverage of this field, not the quality or importance of the discipline.
What kind of structure is this field?
Interpreted from the current atlas — how this field is represented, not a judgement of the field.
Representation health 72/100 — healthy representation. Strongest: evidence, cross-disciplinary. Thinnest: factual depth.
structural · Measures the current Thinking OS representation, not the quality or importance of the field.
Its 72 representation-health is above the 59 median of 51 similarly-sized disciplines (comparable by concept count).
structural · Measures the current Thinking OS representation, not the quality or importance of the field.
72% of its relations reach into 71 other disciplines — an outward-facing field in the atlas.
structural · Measures the current Thinking OS representation, not the quality or importance of the field.
Current atlas gaps: no dated concepts.
atlas representation · Measures the current Thinking OS representation, not the quality or importance of the field.
29% of its concepts have a single connection (mean internal degree 1.9) — a fairly cohesive internal structure.
structural · Measures the current Thinking OS representation, not the quality or importance of the field.
How this lens connects
The kinds of relationship that characterise this discipline lens in the current slice.
Coverage matrix
How these concepts distribute across domains and concept families — real counts, not a score.
Ideas that connect this discipline outward
Concepts viewed through this lens that reach disciplines it does not itself carry — concept-level bridges (distinct from the discipline-to-discipline bridges below).
- Machine learningreachesAlgorithmsBiochemistryBiologyBiotechnologyBusinessCognitive ScienceData ScienceDesignDiscrete MathematicsEconomicsEducationEducational ScienceEngineeringEvolutionary BiologyHuman-Computer InteractionLogicMathematical ModellingMathematicsNeuroinformaticsNeuroscienceOptimizationProbabilitySoftware EngineeringSystems BiologySystems EngineeringSystems ScienceTheory of Computation
- ProbabilityreachesActuarial ScienceArtificial IntelligenceBiostatisticsComplexity ScienceComputer ScienceData ScienceDecision TheoryDiscrete MathematicsEconomicsEpistemologyEthicsFinanceInformation TheoryMachine LearningMedicinePhysical ChemistryPhysicsPublic PolicyQuantum PhysicsStatistical PhysicsThermodynamics
- RiskreachesActuarial ScienceBusinessData ScienceEntrepreneurshipEnvironmental ScienceEpidemiologyEpistemologyMachine LearningMathematicsNetwork SciencePhotographyProbabilityPublic HealthToxicology
- CorrelationreachesBiostatisticsEconometricsEconomicsEpidemiologyEthicsFinanceHistoryMathematicsMedicineMetaphysicsPhilosophy of SciencePsychologyPublic Policy
- PredictionreachesBiologyCognitive ScienceComputer ScienceData ScienceEducational ScienceEpistemologyHistory of ScienceLogicMachine LearningMathematical ModellingPhysicsSystems Science
- OverfittingreachesBiologyBusinessCognitive ScienceComputer ScienceDesignEconomicsEngineeringEthicsPolitical SciencePublic PolicySystems Engineering
Mental models that recur here
Probability ×11
A way to reason about uncertainty by assigning each possible outcome a share of the whole, between impossible (0) and certain (1).
Cause and effect ×8
One thing genuinely bringing about another — as opposed to two things merely moving together. Establishing it needs a mechanism and controls, not just a pattern.
Signal vs noise ×6
Real data mixes a meaningful pattern (signal) with random variation (noise); the skill is telling them apart before you act on either.
Information ×3
Anything that reduces uncertainty. It can be encoded into a signal, sent across a channel, and decoded — and noise can corrupt it on the way.
Learning journeys that use it
People represented in this atlas
People tagged with this lens who have a recorded contribution. Not exhaustive, and not a ranking.
- Carl Friedrich Gauss
1777–1855
- Francis Galton
1822–1911
- Andrei Andrejewitsch Markow
1856–1922
- Ronald Fisher
1890–1962
- George Kingsley Zipf
1902–1950
- Stanisław Ulam
1909–1984
- John Tukey
1915–2000
- Judea Pearl
1936–present
- Vladimir Vapnik
1936–present
- 34 concepts are viewed through this lens.
- 21 of them bridge into other disciplines.
- Its signature thinking pattern is “Probability” (recurs in 11 concepts).
- The most common kind of connection here is “Cause & effect”.
- It is most tightly linked to Mathematics.
Derived from the current graph structure — observations, not a judgement of the field.