Interconnected concepts that bridge engineering precision with human-centered design. Each concept links to related ideas, creating a web of knowledge that spans from structural analysis to cognitive science to product optimization.
Explore how concepts connect across disciplines. Each node represents a concept, with connections showing relationships and bridges between fields.
Statistical methods, experimental design, and data analysis principles
The practical importance of changes in user behavior, focusing on whether differences actually matter to users and business outcomes
Behavioral Significance = f(User Perception × Business Impact × Implementation Cost) A standardized measure of the magnitude of difference between groups, independent of sample size and statistical significance
Cohen's d = \frac{\bar{x}_1 - \bar{x}_2}{s_{pooled}} A measure indicating whether observed differences between groups are likely due to chance or represent genuine effects
p-value = P(observed data | null hypothesis is true) A statistical framework for A/B testing that incorporates prior knowledge and provides probability-based decision making rather than binary significance testing
P(θ|data) = P(data|θ) × P(θ) / P(data) Core concepts from structural engineering, systems thinking, and computational methods
A computational method that divides complex structures into smaller, manageable elements to solve engineering problems with mathematical precision
K × u = F (where K is stiffness matrix, u is displacement vector, F is force vector) The process of breaking down complex structures into smaller, manageable elements for analysis
∑(elements) = Total Structure User experience, attention modeling, and behavioral optimization
Systematic approaches to gather deep insights about user behavior, motivations, and experiences to validate and contextualize quantitative experiment results
Qualitative Insights = User Behavior + Context + Motivation + Experience The smallest meaningful improvement threshold for product metrics that justifies implementation effort and resource allocation
MVE = Implementation Cost / (User Base × Revenue Impact Per User) Structured frameworks for evaluating the financial and strategic value of experiments to prioritize optimization efforts and resource allocation
ROI = (Benefits - Costs) / Costs × 100% The process of dividing time-based content or experiences into discrete segments for analysis, optimization, and prediction
Total Content = Σ(Segment₁ + Segment₂ + ... + Segmentₙ) Understanding how the human mind processes information and makes decisions
How mental processing effort is allocated across different cognitive systems over time, similar to stress distribution in structural engineering
CL(t) = Σ(System1(t) × 0.3 + System2(t) × 1.0) Daniel Kahneman's dual-process theory describing two distinct modes of human thinking: fast, automatic System 1 and slow, deliberate System 2
Total Cognitive Capacity = System 1 (Automatic) + System 2 (Controlled) Key connections between engineering principles and product management insights.
Engineering's spatial division methods applied to time-based content analysis
How forces spread through materials parallels mental effort allocation
Moving beyond p-values to understand what actually matters to users
Cognitive science principles applied to user experience and business decisions
Start with your area of interest—engineering, cognitive science, statistics, or product management.
Each concept links to related ideas. Follow these connections to discover new perspectives.
Look for concepts that bridge disciplines—these often provide the most innovative insights.
Use these concepts to improve decision-making, optimize user experiences, and solve complex problems.