How Can You Tell Whether an A/B Testing Case Study Is Trustworthy?
Evaluate any A/B testing case study with a 12-point evidence checklist covering source, sample, metrics, stopping, SRM, limitations, and transfer.
Read Article→
Free Learning · Better Decisions Make Growth Decisions
Make Growth Decisions
You Can Defend
Know what to test, when to trust the result, and what to do next. Practical guides for analysts, growth teams, and founders.
Which Navigation A/B Test Wins: Visible, Collapsed, or Removed?
Run a cleaner navigation A/B test with visible, collapsed, and removed treatments, precommitted metrics, guardrails, SRM checks, and decisions.
Read Article→
How Do You Optimize a Pricing Page Without Hiding What Buyers Need?
Pricing page optimization should reduce decision work without hiding comparison context. See public evidence, portfolio patterns, and a test plan.
Read Article→
Should a Landing Page Have Navigation—or Is It Costing Sales?
Should a landing page have navigation? Compare the public evidence, missing methods, intent conditions, guardrails, and a safer A/B test plan.
Read Article→
How Should You Design a Checkout Page Without Removing Trust?
Design a focused checkout page without removing trust, recovery, or control. See the research, evidence limits, guardrails, and test plan.
Read Article→
Four A/B Testing Examples—and How Much You Should Trust Them
See four A/B testing examples graded by evidence quality, with missing data, limits, transferable lessons, and safer next-test plans.
Read Article→
Sequential Testing and the SPRT: How to Stop a Test Early Without Cheating
Peeking at a fixed-sample A/B test inflates false positives. Sequential testing lets you check results repeatedly and stop early without cheating.
Read Article→
Triangulation Over Isolation: Building Confidence from Weak, Convergent Signals
A single underpowered test never proves anything alone. How senior practitioners stack weak, independent signals until they converge into real confidence.
Read Article→
The Meta-Analysis Your Experimentation Program Is Missing
Most programs audit individual tests, almost none audit the program itself. A quarterly portfolio audit answers what leadership actually wants asked.
Read Article→
Why Most 'Wins' Don't Replicate: The Winner's Curse, Applied to Growth Teams
The winner's curse means shipped A/B test wins systematically overstate their true effect. The fix: track predicted lift against realized lift over time.
Read Article→
The Confidence Tier Model: How to Decide When Your Data Isn't Enough
Most testing programs are built for traffic they don't have. Three confidence tiers — proven, directional, speculative — each with its own bet-sizing rule.
Read Article→
Decide What Counts as a Win Before You Test
Medicine proved that picking your primary metric after seeing the data is a structural bias. The five-minute fix most experimentation programs skip.
Read Article→
How to Tell If a Growth Hire Understands Risk
A great win story tells you almost nothing about judgment. Two borrowed interview probes — from forecasting research and intelligence tradecraft — do.
Read Article→
Why AI Coding Agents Keep Duplicating Your API Keys
Isolated AI coding sessions can't see your main .env file, so they quietly mint duplicate API keys instead of asking. The mechanism, diagnostic, and fix.
Read Article→
Subscription or Pay-Per-Token? I Audited My Own Claude Code Usage to Find Out
I ran my real Claude Code usage through live API pricing to see if my $200/month subscription was actually a good deal. The gap was bigger than I expected.
Read Article→
7 Examples of Behavioral Economics That Explain Bad Choices
Real examples of behavioral economics, ranked by evidence: which biases replicate at scale and which collapse under scrutiny.
Read Article→
How to Resolve a Git Merge Conflict Without Guessing What the Other Side Was Trying to Do
A clean merge isn't proof it's correct. Here's how to investigate what changed on each side — and the one conflict type worth refusing to auto-resolve.
Read Article→
Why Trusting an AI Assistant's Memory Is the Wrong Default (And What to Check Instead)
An AI assistant answers fluently whether a fact is current or stale. Here's the rule for knowing what to verify live instead of trusting memory.
Read Article→
How to Get Your AI Coding Assistant to Catch Its Own Mistakes Before You Do
The AI that wrote your draft is the worst reviewer of it. Here's the independent-review technique that catches what a second read-through misses.
Read Article→
Behavioral Economics Examples That Explain Your Bad Decisions
Behavioral economics examples reveal why even Microsoft's experiments succeed only a third of the time. Learn what actually works and why.
Read Article→
How the Behavioral Economy Quietly Shapes Your Choices
Every product is already a behavioral intervention. Learn why most fail, and how founders can govern behavioral economics before it governs users.
Read Article→
What the Behavioral Economics Definition Really Means for You
Behavioral economics definition explained: why it's not just bias lists, and how to test if it actually works on your own users.
Read Article→
How Loss Aversion Quietly Shapes Your Decisions
Loss aversion makes losses feel 2x stronger than gains—and it's secretly shaping how leaders judge experiments, hire talent, and kill good programs.
Read Article→
The Action-Velocity Signal: Why Raw Token Counts Lie About AI Agent Costs
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.
Read Article→
Better Decisions
Make Growth Decisions
You Can Defend
Know what to test, when to trust the result, and what to do next. Practical decision guides for analysts, growth teams, and founders. Free. Weekly.
Opens Substack to confirm your subscription · Free · Unsubscribe anytime