Thinking·OS

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Causal Inference depends on Regression analysis. Activate to inspect this relation.Confidence interval is derived from Sampling distribution. Activate to inspect this relation.Econometric model depends on Regression analysis. Activate to inspect this relation.Effect size influences Confidence interval. Activate to inspect this relation.Hypothesis testing applies to Maximum Likelihood Estimation. Activate to inspect this relation.Hypothesis testing applies to Regression analysis. Activate to inspect this relation.Hypothesis testing depends on Sampling distribution. Activate to inspect this relation.Logistic regression is a Regression analysis. Activate to inspect this relation.Maximum Likelihood Estimation enables Regression analysis. Activate to inspect this relation.Meta-analysis applies to Randomized controlled trial. Activate to inspect this relation.Meta-analysis measures Effect size. Activate to inspect this relation.Multiple comparisons problem constrains Hypothesis testing. Activate to inspect this relation.Multiple comparisons problem influences P-value. Activate to inspect this relation.Ordinary Least Squares is part of Regression analysis. Activate to inspect this relation.P-value is part of Hypothesis testing. Activate to inspect this relation.Randomized controlled trial applies to Hypothesis testing. Activate to inspect this relation.Randomized controlled trial constrains Confounding. Activate to inspect this relation.Regression analysis constrains Confounding. Activate to inspect this relation.Regression analysis is a Mathematical model. Activate to inspect this relation.Regression analysis models Correlation. Activate to inspect this relation.Statistical power depends on Effect size. Activate to inspect this relation.Statistical power measures Hypothesis testing. Activate to inspect this relation.Survival analysis is analogous to Regression analysis. Activate to inspect this relation.Time series analysis is a Regression analysis. Activate to inspect this relation.Effect sizeConfidence intervalMeta-analysisStatistical powerSampling distributionRandomized controlled trialHypothesis testingConfoundingMaximum Likelihood EstimationRegression analysisMultiple comparisons problemP-valueCorrelationCausal InferenceEconometric modelLogistic regressionOrdinary Least SquaresSurvival analysisTime series analysisMathematical model
Relationship types

15 concepts viewed through this lens. Bridge concepts connect this view to Biology, Computational Biology, Data Science, Econometrics….

Legend
  • Focused concept
  • Connected concept
  • Bridge concept (just outside the lens)
  • Arrow points from cause / source to effect / target
  • A line with no arrow is a two-way relationship
  • Node colour marks the concept’s primary discipline
20 concepts24 relationships10 disciplines6 relation families

Effect size

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At a glance

Effect size is a quantitative measure of the magnitude of a phenomenon, independent of sample size.

Disciplines
Biostatistics
Role in the graph
Connector
Relationships
3 · 3 relation families

What am I looking at?

In this lens (15)

Bridge concepts (9)

Just outside the lens — they connect it to other context.

Insights from this view

Structural observations about the concepts shown here — descriptions of this graph, not claims about the world.

  • This view connects 10 disciplines: Biostatistics, Data Science, Econometrics, Economics, Epistemology, Mathematical Modelling, Mathematics, Probability, Statistics, Systems Science.
  • Correlation is a bridge concept — viewed here through Data Science, Epistemology, Probability, Statistics.
  • The connections here span 6 relation families.
  • Causality explains 3 concepts in this view (Biostatistics, Data Science, Econometrics, Epistemology, Mathematical Modelling, Mathematics, Probability, Statistics, Systems Science).

Relationships as a list

The focused concept’s relationships. Pick another concept in the graph above to update this list.

Explore through a different lens

A lens is a deterministic projection of the graph. Pick a discipline, thinking pattern or journey to reframe the whole view.

By discipline

ChemistryBiologyPhysicsMathematicsComputer ScienceGeologyMineralogyEconomics

By thinking pattern

Bayesian updatingCause and effectCompoundingConservationConstraintsDefense in depthDiffusion & spreadEmergenceEnergy transformationEntropy & irreversibilityEquilibriumFeedback loopIncentivesInformationLevels & hierarchyModularityNetworksOptimizationOscillationProbabilityRecursion & self-similarityScaleSignal vs noiseSource–pathway–receptorStocks and flowsSymmetry & invarianceThresholds & tipping pointsTrade-offsTriangulationVariation & selectionWaves

By journey

From a Leaf to the ClimateWhy Music Moves UsHow Machines LearnReasoning about a Leaf and the Climate

Concept collections

Concept collections are curated lenses onto the fabric — themed sets of ideas that recur across disciplines. They are not journeys; they are a way to read the graph.

About this view

What this is

Start from one concept and expand outward. The view never shows everything at once — click a node to refocus, filter by relationship type, or switch to an accessible list.

One fabric

3750 concepts and 5051 typed relations form one connected component — no isolated silo.

How to read it

Focus a concept, or apply a lens (discipline, mental model, journey) to see only the threads that matter.