Scientific & Hypothesis-Driven¶
Treating engineering claims like scientific ones — stated as falsifiable hypotheses, tested with experiments, and grounded in measurement before action — so decisions rest on evidence instead of opinion.
This section applies the scientific method to building software: framing beliefs as hypotheses you could disprove, designing experiments and A/B tests to settle them, measuring before you optimize, and using spikes and prototypes to buy information cheaply. The through-line is replacing "I think" with "I tested, and here's what happened."
Topics¶
| # | Topic | What you'll learn |
|---|---|---|
| 01 | Hypothesis and Falsifiability | Framing claims as testable predictions, falsifiability, what evidence would change your mind, null hypotheses |
| 02 | Experiments and A/B Testing | Controlled experiments, control vs treatment, statistical significance, sample size, confounds and p-hacking |
| 03 | Measure Before Optimize | Profiling before tuning, Amdahl's law, fighting premature optimization, letting data find the bottleneck |
| 04 | Spikes and Prototypes | Time-boxed spikes to retire risk, throwaway vs evolutionary prototypes, buying information, when to discard the code |
How to use this section¶
Each topic has five depth levels — junior → middle → senior → professional — plus an interview Q&A bank and hands-on tasks. Start at your level and climb. Topics 01–02 establish the method; 03–04 are its two highest-leverage applications in day-to-day engineering.
Part of the Engineering Thinking roadmap. Turn the same evidence-driven lens on your own mind with Metacognition & Learning.