What Is Analysis?

Analysis comes from the Greek analýein — “to loosen” or “to break apart.”
At its core, analysis means:

Breaking a complex whole into parts to understand structure, function, and relationships.

In modern knowledge systems, analysis is not just a method — it is a foundational epistemic tool across science, mathematics, engineering, humanities, policy, and technology.

Let’s explore major fields of analysis across domains.

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1. Mathematical Analysis

Core Idea:

Study of limits, continuity, change, and infinity.

Historical Roots:

• Isaac Newton
• Gottfried Wilhelm Leibniz

Major Branches:

• Real Analysis – rigorous calculus, ε–δ definitions
• Complex Analysis – functions of complex numbers
• Functional Analysis – infinite-dimensional spaces
• Harmonic Analysis – Fourier methods
• Measure Theory – integration foundations

Why It Matters:

• Physics (quantum mechanics)
• Engineering (signal processing)
• Economics (optimization models)
• AI (gradient descent, neural networks)

Mathematical analysis studies continuous structure and the behavior of functions under limiting processes.

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2. Data Analysis

Core Idea:

Extracting patterns, trends, and meaning from data.

Components:

• Data cleaning
• Visualization
• Pattern recognition
• Predictive modeling

Modern Tools:

• Python, R
• SQL databases
• Machine learning systems

Applications:

• Business intelligence
• Healthcare diagnostics
• Climate modeling
• Social media analytics

Data analysis transforms raw numbers into actionable knowledge.

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3. Statistical Analysis

Core Idea:

Understanding uncertainty and variability.

Statistical analysis answers:

• Is this effect real?
• Is this difference significant?
• How confident are we?

Major Areas:

• Descriptive statistics
• Inferential statistics
• Bayesian analysis
• Regression analysis
• Time series analysis

Statistics provides the logic of inference under uncertainty.

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4. Psychoanalysis

Founder:

Sigmund Freud

Core Idea:

Analyzing the unconscious mind.

Key Concepts:

• Id, Ego, Superego
• Repression
• Dream symbolism
• Defense mechanisms

Later developments:

• Carl Jung
• Jacques Lacan

Psychoanalysis studies hidden mental structures influencing behavior.

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5. Behavioural Analysis

What Is Behavioural Analysis?

Behavioural analysis is the systematic study of observable behavior — how it is formed, maintained, modified, and predicted.

At its core:

Behaviour is shaped by interaction between the organism and its environment.

Unlike psychoanalysis (which focuses on unconscious motives), behavioural analysis focuses on observable actions and measurable patterns.

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Origins: Behaviourism

Major Figures

• Ivan Pavlov – Classical conditioning
• John B. Watson – Founder of behaviorism
• B. F. Skinner – Operant conditioning

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Core Theoretical Foundations

A. Classical Conditioning (Pavlov)

Learning by association.

Stimulus → Response
Bell → Salivation (after repeated pairing with food)

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B. Operant Conditioning (Skinner)

Learning by consequences.

Behaviour → Consequence → Future Probability

Four key outcomes:

• Positive reinforcement
• Negative reinforcement
• Punishment
• Extinction

This framework mathematically resembles feedback systems in physics and cybernetics.

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Applied Behaviour Analysis (ABA)

Applied Behaviour Analysis (ABA) uses reinforcement principles to change behavior.

Common Applications:

• Autism interventions
• Classroom behavior management
• Organizational performance
• Addiction treatment
• Habit formation

ABA is highly data-driven — behavior is measured, graphed, and optimized.

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Modern Behavioural Analysis Fields

Behavioural analysis has expanded far beyond early behaviorism.

Behavioural Economics

Daniel Kahneman
Amos Tversky

Studies systematic deviations from rational decision-making.

Examples:

• Loss aversion
• Framing effects
• Cognitive biases

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Behavioural Neuroscience

Studies neural mechanisms behind behavior.

Brain regions, neurotransmitters, and circuits are mapped to actions.

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Behavioural Data Science

• Social media behaviour modeling
• Consumer analytics
• Predictive policing (controversial)
• AI behavior modeling

Here behaviour becomes large-scale statistical pattern analysis.

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Organizational Behaviour Analysis

Used in:

• Leadership studies
• Incentive design
• Performance systems
• Corporate culture analysis

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Analytical Framework: The ABC Model

A core tool in behavioural analysis:

A – Antecedent (What happens before?)
B – Behaviour (What is the action?)
C – Consequence (What happens after?)

Example:

Antecedent: Stressful meeting
Behaviour: Raises voice
Consequence: Others withdraw

Over time, consequences reinforce or weaken behaviours.

This is structurally similar to dynamical systems:

Environment → Action → Feedback → State update

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Quantitative Behavioural Analysis

Modern behavioural analysis uses:

• Time-series analysis
• Reinforcement learning models
• Markov decision processes
• Bayesian inference

In AI, reinforcement learning directly inherits Skinnerian logic:
Agent → Action → Reward → Policy update.

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Criticisms of Behavioural Analysis

• Early behaviorism ignored internal cognition
• Ethical concerns in manipulation
• Cultural variability
• Over-reliance on observable metrics

Modern approaches integrate cognition, emotion, and neurobiology.

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Behavioural Analysis vs Psychoanalysis

Behavioural Analysis	Psychoanalysis
Observable behaviour	Unconscious motives
Data-driven	Interpretive
Environmental focus	Intrapsychic focus
Experimental	Clinical narrative

They represent two different epistemological approaches to understanding human action.

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Modern Frontier: Behavioural AI

• AI agents trained via reward signals
• Human behavior prediction systems
• Algorithmic nudging
• Digital reinforcement environments

Behavioural analysis now operates at planetary scale via platforms and algorithms.

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6. Systems Analysis

Core Idea:

Understanding interconnected components in complex systems.

Influences:

• Ludwig von Bertalanffy (General Systems Theory)
• Norbert Wiener (Cybernetics)

Applications:

• Ecology
• Organizational management
• Software engineering
• Policy modeling

Systems analysis emphasizes relationships and feedback, not isolated parts.

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7. Philosophical Analysis

Core Idea:

Clarifying concepts through logical decomposition.

Tradition:

• Bertrand Russell
• Ludwig Wittgenstein

Used to:

• Analyze language
• Clarify meaning
• Examine logical structure

Philosophy treats analysis as conceptual precision.

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8. Financial Analysis

Core Idea:

Evaluating financial performance and risk.

Tools:

• Ratio analysis
• Discounted cash flow
• Risk modeling
• Portfolio theory

Used in:

• Investment
• Corporate finance
• Economic forecasting

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9. Computational Analysis

Modern hybrid field combining:

• Algorithms
• Simulation
• Numerical methods

Used in:

• Computational physics
• AI systems
• Cryptography
• Climate models

Computational analysis extends mathematical reasoning into large-scale digital environments.

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10. Comparative & Cultural Analysis

Used in:

• Literature
• Anthropology
• Political science

Compares structures across cultures to find:

• Patterns
• Archetypes
• Power structures

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Ifanalysis: A Unified Model of Analysis

Across fields, analysis always involves:

1. Decomposition — breaking into parts
2. Structure Identification — finding patterns
3. Relation Mapping — studying interactions
4. Inference or Interpretation — drawing meaning

In mathematics → limits and structure
In statistics → uncertainty and inference
In psychology → unconscious structure
In data science → pattern extraction
In philosophy → conceptual clarity

Orisa Analysis is the dual of Ifa Analysis. Key examples of Orisanalysis: Osun analysis, Ogun analysis, Obatala analysis, Odu analysis, etc.