Research Methods

If you could invest in one thing to live a longer, healthier, and happier life, what would it be? For more than 85 years, researchers at Harvard followed hundreds of people across their entire lives, tracking everything from their physical health and mental well-being to their careers, relationships, and aging. It became the longest-running study of adult development in history. After decades of data, one finding stood above the rest: The strongest predictor of a long, healthy, and fulfilling life wasn't wealth, fame, social status, cholesterol levels, or genetics. It was the quality of your relationships. The people who were most connected to family, friends, and community lived longer, stayed healthier, and were happier as they aged. In fact, participants who were most satisfied in their relationships at age 50 were the healthiest at age 80. The researchers also found that close relationships helped protect the brain. People with strong, supportive connections experienced less cognitive decline and maintained better memory and mental function later in life. Investing in meaningful relationships may be one of the most powerful things you can do for your longevity, happiness, and overall well-being.

This is the most underrated way to use Claude: (and it has nothing to do with writing or coding) It's competitive intelligence. Using data that's free, public, and updated every single week. Here's my extract step by step guide: Step 1. Go to claude .ai. Step 2. Select the new Claude "Opus 4.6." Step 3. Turn on "Extended Thinking." Step 4. Pick a competitor. Go to their careers page. Step 5. Copy every open job listing into one doc. (Title. Team name. Location. Full description) Step 6. Save it as one .txt or .docx file. Step 7. Search the company at EDGAR (sec .gov) Step 8. Download its recent 10-K or 10-Q filing. (Official strategy, risks, and financials - all public.) Step 9. Upload both files to Claude Opus 4.6. Step 10. Paste this exact prompt: "You are a competitive intelligence analyst at a rival company. I've uploaded [Company]'s complete current job listings and their most recent SEC filing. Perform a strategic intelligence analysis: → Cluster these roles by what they suggest is being built. Don't use the team names they've listed. Infer the actual product initiatives from the skills, tools, and responsibilities described. → Identify capabilities or teams that appear entirely new — not mentioned anywhere in the SEC filing. These are unreleased bets. → Find roles where seniority is disproportionately high for a new team. This signals executive-level priority. → Cross-reference the SEC filing's Risk Factors and Strategy sections with hiring patterns. Where are they investing against a stated risk? Where did they flag a risk but have zero hiring to address it? → Predict 3 product launches or strategic moves this company will make in the next 6-12 months. State your confidence level and cite specific job titles and filing sections as evidence. Format this as a 1-page competitive intelligence briefing for a CMO." What you'll find: → Products that don't exist yet but will in 6 months. → Priorities that contradict what the CEO said. → Risks they told the SEC but aren't addressing. This is what consulting firms charge $200K for. It took me 10 minutes. I used the new Claude 'Opus 4.6' for a reason: ✦ It read 60 job listing & a 200-page filing together.  ✦ And connects dots across both. ✦ It is superior in thinking and context retrieval. That's why I didn't use ChatGPT for this.

🧪 Useful Calculators For UX Research. Helpful tools and guides to estimate the right number of participants for surveys, card sorting and usability testing. 🤔 Testing isn’t about finding universal truths, but key blockers. 🚫 Scale ≠ clarity: we must know what we’re trying to learn first. ✅ Iterate with 5 people at a time: test, adjust, test again. ✅ Surveys: aim for confidence level 95%, margin of error 2–5%. ✅ With 10.000 users, you will need ≥567 answers to reduce bias. ✅ Assume the response rate of 20–30% (incl. no-show-rate). ✅ To get reliable survey results, we need to invite 2835 people. ✅ For card sorting, get 15–30+ people to sort independently. ✅ For tree testing, invite at least 25 (better: 50) participants. ✅ Nothing matters more than targeted and diverse sample. I absolutely love Nikki Anderson’s point about big sample sizes often wrongfully viewed as the “safest” way to discover insights. They can’t fix vague goals, at times answer wrong or misleading questions, often skip the difficult part of framing the problem first. Scale sounds impressive, but it doesn’t map to clarity. Nikki suggests to start with a decision of what we’re trying to learn first, then the question, then the method and then the sample size. But we must know — and be committed — what want to know first. And very often we don’t need statistically significant results to notice and act on critical blockers. Research doesn’t have to be expensive or time-consuming. In the worst case, I start with 5×45 mins interviews to spot critical blockers and unmet user needs. As we run sessions, I mark critical areas and record short screen share snippets — with consent — and make them visible in the company. Once you’ve built enough confidence in the work that you are doing, it will be much easier to ask for bigger commitments — in fact, you might be surprised by how quickly your research work will be requested, rather than merely applied to design work. --- Useful resources: 👥 Qualitative Sample Size Calculator, by UserInterviews https://lnkd.in/enNhnjmZ 💸 Research Incentive Calculator https://lnkd.in/dZim2YSq 🔘 Survey Sample Size Calculator https://lnkd.in/e8htudk3 💯 Bonus: Design System ROI Calculator, by Knapsack https://lnkd.in/eYCxBTGt 📱 UX Work ROI Calculator, by Paul Boag https://lnkd.in/eAXtXWf5 🚀 UX Research Project Launch Kits https://lnkd.in/eX2zt88x 🌲 UX Research Field Guide, by UserInterviews