research-methods
11 free lessons tagged research-methods across Business, AI, Robotics. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Costs, Capacity, and a Protocol You Can Trust
The edge that survives statistics still has to survive trading. Spread, market impact and the square-root law, why every strategy has a capital ceiling, and the research protocol that makes a backtest worth believing.
Selection Bias and the Deflated Sharpe Ratio
The statistical core of backtest overfitting: why the best of many trials is inflated even when nothing works, how much to discount it, and why finance needs a far higher significance bar than the usual one.
The Biases That Break It Before Statistics
Look-ahead bias, survivorship bias, and point-in-time data. The errors that make a backtest wrong as a simulation, independent of any statistical question about whether the edge is real.
What a Backtest Actually Claims
A backtest is not a measurement of the past, it is a counterfactual about a world that never happened. Getting precise about that claim explains every way backtests mislead.
Causal Discovery: Learning the Graph
If the graph is an assumption, can you learn it from data instead? Conditional independence testing, the equivalence classes that limit what is knowable, and what changes when unmeasured confounders are allowed.
Identification: When Observational Data Is Enough
The central question of causal inference has a precise answer. The backdoor criterion, the front-door criterion, instrumental variables, and what to do when no identification strategy exists.
Why Correlation Is Not Enough
The formal machinery that makes causal questions answerable: structural causal models, graphs as assumptions you can inspect, the three ways variables become associated, and why prediction and intervention are different problems.
Why the Standard Metrics Mislead
Displacement error is the field's default metric and it rewards the wrong behaviour: hedged average predictions, physically impossible trajectories, and best-of-many reporting that flatters diverse nonsense. What to measure instead.
Real Markets and Serious Pushback
What happened to margins when German petrol stations adopted pricing software, and the substantial body of research arguing the simulation results are fragile, parameter-dependent, and may not survive contact with real markets.
The Average Trap, Model Collapse, and Responsible Practice
Two failure modes that survive perfect methodology: a prediction objective that pulls research toward the average consumer, and a feedback loop where synthetic data degrades the evidence base. Plus the guidelines for using this responsibly.
The Analytic Flexibility Problem
Silicon samples require dozens of defensible setup choices, and those choices change the answer. The study that generated 252 configurations, found correlations ranging from .23 to .84, and what it means for anyone reporting synthetic results.