Problem Framing & Analytical Design
Translate an organisational or scientific question into a precise analytical problem with a target, unit of analysis, decision context and success criterion. The topic is split into focused lessons so definitions, implementation decisions, diagnostics and common failure modes can be learned separately.
Learn the mechanism one decision at a time
Work through the lessons in order if the topic is new. If you already know the basics, open the specific leaf lesson that matches the operation, diagnostic or failure mode you need.
1Definition→
2Mechanism→
3Example→
4Diagnostic→
5Decision
Define the decision or research questionStart with the decision, hypothesis or action the analysis must support, not with an available dataset. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
02Define the unit of analysisThe unit of analysis is the entity represented by one prediction or observation at decision time. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
03Construct the targetTarget construction converts the real-world outcome into a measurable label or value with a clear observation window. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
04Choose success criteriaTechnical metrics should be connected to practical cost, risk, capacity or scientific objectives. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
05Plan the experimentDecide how data will be split, what baselines are needed, which comparisons are fair and what information must remain blinded. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.