Articles exploring decision-making through the lens of behavioral science and experimentation. Practical frameworks for growth leaders who measure in revenue, not vanity metrics.
22 articles
Token totals and dollar totals are the metrics everyone reaches for first when auditing AI agent spend — and they are frequently the wrong ones. A better diagnostic, and a portfolio-style framework for model selection.
Most people accept an AI agent’s first answer. A three-layer audit — primary-source check, realistic-input test, goal re-derivation — catches what pattern-matching misses.
One honest experiment result can get a growth program funded or gutted. The fix isn't better reporting — it's pre-registration, borrowed from clinical trials.
The most expensive misreading in A/B testing is treating 'not statistically significant' as 'no difference.' It actually means 'we didn't collect enough…
Personality tests give you a label. Labels don't change behavior. What works: functional bottlenecks, hard constraints, and feedback loops.
Your AI optimizes for speed, not truth. Here's why it confidently lies about real-time data and the prompting fixes that force verification.
Most non-execution is risk management in disguise. The fix: cut scope until shipping becomes the path of least resistance.
Most recession forecasts fail because they treat deterioration as breakdown. Track income, spending, and credit — ignore everything else.
Smart people build systems that optimize storage instead of throughput. Here's why organization backfires and constraint-based execution wins.
A data analyst's real job is not producing dashboards. It is helping stakeholders make better decisions with data.
Most experimentation advice assumes perfect statistical significance. Here is how to make the best decision when the data will never be complete — a…
Flat A/B test results are undervalued. Learn why a zero-lift outcome carries real strategic value and how to extract actionable insights from null results.
Inconclusive A/B tests are not failures. Learn why tests end without a clear winner and the strategic decisions you should make when results are ambiguous.
When A/B tests track multiple metrics, statistical complexity increases. Learn frameworks for managing metric conflicts and making sound decisions.
Confidence intervals tell you more than p-values ever could. Learn how to read them, use them for decisions, and avoid the common misinterpretations teams make.
AI makes you faster, but faster is not always better. A contrarian take on the AI productivity illusion and when slowing down produces superior outcomes.
A/B testing isn't free. Learn the real costs — opportunity cost, engineering resources, decision delay — and develop the judgment to know when shipping fast…
Challenge the myth that B2B buying is purely rational. Explore why high-stakes business decisions are deeply emotional and how emotional design patterns can…
Self-service vs. high-touch through the lens of decision complexity, perceived risk, and social proof needs.
Multiple decision-makers with conflicting motivations require different persuasion strategies than B2C.
The availability heuristic explains why easily recalled information disproportionately influences risk perception.
Humans process negative information more deeply than positive information, which means a single critical review carries disproportionate weight in purchase…