Analyzing Personal Bias Patterns

That VC asked who was picking up my kids. So I started tracking every bias. 165 investor meetings. 73 inappropriate questions. I documented them all. The data will make you angry. Good. 𝗧𝗵𝗲 𝗯𝗶𝗮𝘀 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻: • 31 asked about childcare arrangements • 19 questioned my "work-life balance" • 14 asked if my husband was "okay with this" • 9 wondered how I'd handle travel with kids 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗸𝗶𝗰𝗸𝗲𝗿: VCs who asked about my kids? 0% conversion. VCs who asked about unit economics? 23% conversion. Meeting #47: "How does your husband feel about you running the company?" Meeting #48: Pitched to his rival. Got a cheque. 𝗜 𝗯𝘂𝗶𝗹𝘁 𝗮 𝗯𝗶𝗮𝘀 𝘀𝗰𝗼𝗿𝗲𝗰𝗮𝗿𝗱: -10 points for each personal question -20 points for childcare concerns -30 points for "husband" questions -50 points for suggesting I hire a male CEO The worst offender? -140 points. Still took the meeting. Still said no to their offer. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗹𝗲𝗮𝗿𝗻𝗲𝗱: Bias is predictable. Track it. Your time has value. Protect it. Their questions reveal their thinking. You don't need their approval. The pattern is clear: Investors who focused on my personal life weren't serious about my business. 𝗦𝗼 𝗜 𝗰𝗵𝗮𝗻𝗴𝗲𝗱 𝗺𝘆 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵: Created pre-meeting filters Asked my own screening questions Walked out of 3 meetings (yes, really) Turned down two term sheets (painful but necessary) Only engaged high-conviction investors Your kids aren't a liability. They're watching you build empires. What's the worst bias you've faced in a pitch? #BiasInVC  #FemaleFounders  #FundraisingData

The 5 psychological traps that almost destroyed my design career before I recognized them. When I started Essajees Atelier, I thought my biggest challenge would be scaling or handling difficult clients. In reality, my own thought patterns nearly derailed it. Looking back, I recognize the psychological traps I fell into and learned to avoid: 1/ Anchoring: The first impression or advice I received would influence every decision that followed. Sometimes even when better opportunities were in front of me. I broke free when I started testing decisions against objective data rather than first impressions. 2/ Confirmation bias: I gravitated toward information that reinforced what I already believed, dismissing valuable insights that could have helped me pivot. Challenging myself to seek opposing viewpoints forced me to make more balanced, informed choices. 3/ Reactance: The moment someone told me what I "should" do, I instinctively resisted, though it was actually sound advice. Reframing suggestions as possibilities rather than directives helped me evaluate them with an open mind. 4/ Backfire Effect: When my assumptions were challenged, my first instinct was to defend them instead of reconsidering the flaws. I started asking, "What if I'm wrong?" and it turned defensiveness into curiosity. 5/ Negativity Bias: A single bad experience or failed idea carried more weight in my mind than dozens of successful projects. Keeping a record of wins helped me rewire my thinking to focus on the bigger picture. Understanding these patterns transformed my business approach, decision-making, and client relationships. After implementing systems to counter these, our client satisfaction increased 4x according to surveys. The most valuable lesson was that success = talent.  It’s recognizing when your own mind is working against you. Which of these traps have affected your decisions? #business #mindset #interiordesign #growth

The biggest trap in data analysis isn’t the wrong formula. It’s our own bias. I once worked on a sales analysis where I was convinced discounts were driving revenue growth. I built dashboards, charts, and even a solid narrative around it. But when I stepped back, the data told me something else → the real driver wasn’t discounts, it was a newly launched product line. My assumption had blinded me. I wasn’t analyzing — I was validating. That’s the danger of bias in analysis: → We don’t just process numbers. → We process our beliefs, assumptions, and sometimes our ego. The analytical mindset isn’t about proving yourself right. It’s about being willing to be wrong — and letting the data humble you. So the next time you analyze, pause and ask yourself: 👉 Am I searching for the truth? Or am I searching for validation? How to avoid bias in analysis 1️⃣ Start with questions, not conclusions → Frame hypotheses, don’t assume outcomes. 2️⃣ Look for disconfirming evidence → Ask: What would prove me wrong? 3️⃣ Use multiple lenses → Segment data in different ways, cross-check with other sources. 4️⃣ Peer review your work → A fresh set of eyes often spots the blind spots you can’t see.

Bias isn’t always loud. Sometimes it’s silent and that’s the problem. Here’s what Biased by Jennifer Eberhardt taught me and why it changed how I see the world. It’s a book about how all brains work. Most bias isn’t intentional. It’s automatic fast, subtle, and baked into how we process the world. Take this research finding: We’re better at recognizing faces from our own racial group than from others. Not because we’re trying to be unfair. But because our brains are wired to detect familiarity—often unconsciously. The consequences are massive. This bias affects: Who gets hired Who gets searched Who gets punished And all of it happens without us noticing. But here’s what I admire about this book: It doesn’t wallow in guilt. It doesn’t cancel or shame. It says: Bias isn’t a trait—it’s a state. And states can change. That’s the hopeful part. Once we become aware of our patterns, we can: Slow down decisions Rework systems Build environments that reduce bias by design It’s not easy. But it is possible. The takeaway: Bias isn’t just about others. It’s about us. And fixing it starts with seeing it clearly, honestly, and with the belief that change is possible.

I thought my qualitative data was solid. Good on ya. But then reviewers torched it in minutes. The culprit? Invisible biases. (More smart tips in these posters: posters.lennartnacke.com) How I eliminated 7 invisible biases (in just a few wombat naps): 1. The Hawthorne Effect Your participants act differently when observed. Solution: Use unobtrusive measures or extend observation periods. 2. Observer Bias Your expectations shape what you see and record. Solution: Use structured protocols and multiple independent coders. 3. Self-selection Bias Only certain types of people join your study. Solution: Diversify recruitment channels and document participant characteristics. 4. Response Bias Participants tell you what they think you want to hear. Solution: Ask indirect questions and triangulate with other data sources. 5. Nonresponse Bias Certain groups never participate, skewing your sample. Solution: Track and analyze patterns of nonresponse. 6. Pygmalion Effect Your expectations unconsciously influence participant performance. Solution: Standardize interactions and blind researchers when possible. 7. Recall Bias Participants misremember past events. Solution: Use timeline tools and concrete anchoring events. You've gotta find these patterns, So your next paper gets published. Bias is the dropbear waiting in your methods section. Spot it before they do. Research without bias awareness isn't research. It's a confirmatory circlejerk of your beliefs. Don't just collect some data. Question how it reached you. What's the bias that nearly tanked your last study, buddy? #research #phd #bias