Learning Path 25
Evidence, Experiments, and Optimization
Read evidence without being fooled by it, design a test that can actually detect the effect you are hunting, recover a causal answer when you cannot run an experiment at all, then optimize what you found and price it. Written for someone with a P&L rather than a statistics degree.
- Advanced
- ~15h estimated
- 13 modules
- 74 topics
Suggested before this path:
Why this path matters
Most business decisions are made on evidence that would not survive a second look: a test called early, a turnaround that was regression to the mean, a channel credited for demand it did not create. This route is how you tell a real result from a lucky one — and what to do once you can.
What you will understand
- Offers, messages, and funnels that turn strangers into buyers.
- Proving what works — then optimizing it.
Modules in this path
Offers and Positioning · Marketing
- What Marketing Actually Is
- The Offer Comes Before the Ad
- Unique Selling Proposition
- Positioning Basics
- Message-market Fit
- Copywriting Fundamentals
Copy and Creative · Marketing
- Headlines That Get Read
- Landing Pages That Convert
- Conversion Rate Basics
- Funnels Explained
- Lead Magnets
- Calls to Action
Funnels and Conversion · Marketing
- Paid Ads Fundamentals
- Ad Creative That Works
- Targeting versus Creative
- Ad Frequency and Fatigue
- Retargeting
- Organic Content Marketing
Paid Acquisition · Marketing
- Short-form Video Marketing
- Content That Compounds
- Brand versus Direct Response
- Building a Marketing Calendar
- Launches and Promotions
- Discounts Without Cheapening the Brand
Organic and Brand · Marketing
- Email Marketing That Sells
- Retention Marketing
- Word of Mouth by Design
- Testimonials and Case Studies
- Marketing Measurement Basics
- Attribution and Its Limits
Retention and Measurement · Marketing
Reading Evidence · Quantitative Methods
- Sampling Error and How Wrong a Small Sample Can Be
- Statistical Significance and What a P-value Does not Mean
- Confidence Intervals and the Range You Should Have Quoted
- Base Rates and the Prosecutor's Fallacy in Business Data
- Regression to the Mean and the Illusion of a Turnaround
- Correlation, Confounding, and the Causal Question Underneath
Running Experiments · Quantitative Methods
- Designing an A/B Test That Can Actually Detect the Effect
- Statistical Power and the Minimum Detectable Effect
- The Peeking Problem and Why You Cannot Watch a Test Run
- Multiple Comparisons and the False-discovery Rate
- Holdouts and Geo Experiments When You Cannot Randomize Users
- Switchback Tests and Interference Between Treated Units
Causal Inference Without an Experiment · Quantitative Methods
- Difference-in-differences and the Parallel-trends Assumption
- Instrumental Variables and the Exclusion Restriction
- Regression Discontinuity and the Local Average Treatment Effect
- Synthetic Control and the Constructed Counterfactual
- Propensity-score Matching and the Selection-on-observables Bet
- Uplift Modeling and Heterogeneous Treatment Effects
Deciding Under Uncertainty · Quantitative Methods
- Bayesian Updating and the Posterior You Should Have Carried
- Expected Utility and Why a Risk-neutral Firm Is a Fiction
- The Value of Information and What a Test Is Worth Before You Run It
- Multi-criteria Decision Analysis and Weighting Things That Do not Compare
- Robust Optimization and the Price of the Worst Case
- Stochastic Programming and Decisions You Make in Two Stages
Optimization and Operations Research · Quantitative Methods
- Linear Programming and Reading the Shadow Price
- Integer Programming and Why Scheduling Is Hard
- Network Flow and the Vehicle Routing Problem
- The Newsvendor Problem and the Cost of Ordering Wrong
- Queueing Theory and Why Utilization Above 85 Percent Breaks
- The Theory of Constraints and Managing the Bottleneck
Models That Predict · Quantitative Methods
- Supervised Learning and the Bias-variance Tradeoff
- Overfitting, Cross-validation, and the Honest Holdout
- Forecasting Demand and the Limits of a Time Series
- Multi-armed Bandits and the Explore-exploit Tradeoff
- Reinforcement Learning for Sequential Business Decisions
- Model Risk and When a Model Should not Be Allowed to Decide
Pricing and Revenue Management · Quantitative Methods
- Revenue Management and Where Dynamic Pricing Came From
- Estimating a Demand Curve From Your Own Price History
- Protection Levels and Littlewood's Rule for Perishable Capacity
- Surge and Peak Pricing and the Fairness Constraint
- Bandit-based Price Testing Without Burning the Market
- Personalized Pricing and the Legal and Reputational Limits
Suggested tools
Suggested case studies
After this path: Market Design