Logical Reasoning Skills

Most people argue to win. But the smartest people argue to understand. I learned this lesson the hard way. Early in my career, I thought logic was simple: If A, then B. Clear, right? Until one day I lost an important debate with a colleague. Not because I was wrong — but because I was trapped in one way of thinking. That’s when I realized: Logic isn’t one thing. There are at least seven types. And each one offers a new lens on truth. — Deductive Logic gives certainty. — Inductive Logic builds patterns. — Abductive Logic finds the best explanation from incomplete data. — Modal Logic weighs possibility vs. necessity. — Fuzzy Logic embraces shades of gray. — Dialectical Logic evolves through contradictions. — Informal Logic drives our everyday decisions. 📊 Want to dive deeper? See my infographic below — with diagrams and examples of how each type works. Since then, I stopped asking: “Am I right?” And started asking: “Which type of logic am I using?” Because the real skill isn’t to “win” an argument. The real skill is to choose the right logic for the right moment. That’s how leaders make smarter decisions, how innovators uncover hidden patterns, and how negotiators build common ground. 💡 Next time you face a tough choice — ask yourself: What logic am I applying right now? — Natan Mohart

Leaders are often celebrated for their "gut instinct," but between a hunch and a great decision lies a bridge forged in logic. While instinct may be where a leader's thinking begins and ends, the true engine of rational decision-making is the consistent practice of logical reasoning. This isn't about rigid, academic formality. It's about a clear-eyed approach to how we navigate complex problems. The two most powerful tools in a leader's arsenal are deductive and inductive reasoning. Understanding the difference between them is the first step toward mastering both. Deduction: Think of this as a top-down process. It's about starting with a general principle or theory and applying it to a specific case to reach a logical conclusion. If your initial principle is true, your conclusion must also be true. Induction: This is a bottom-up process. It’s about taking a series of specific observations to form a general theory or hypothesis. It is less certain than deduction, as the conclusion is only probable, not guaranteed. If you're a grad student, you can apply this to your job search. The Deductive Approach: You might start with a general assumption: "Most companies in this industry hire from a small list of target schools." You check the company’s career page, and your school is not on it. Deductively, your conclusion is that your chances are low. The Inductive Approach: You notice the company recently hired three people from your school for other roles. You see the job description emphasizes skills you have. You connect with a recruiter who says the company is open to non-target applicants. Inductively, you infer a new theory: this company is more flexible than the common assumption suggests, and your chances may be better. The Power of Both The best leaders use both forms of reasoning. Deductive reasoning helps you filter out noise, avoid dead ends, and make quick, logical decisions based on established facts. Inductive reasoning, on the other hand, helps you test assumptions, find new opportunities, and discover a path where none seemed to exist. Critical thinking isn’t about choosing one over the other; it’s about knowing when to use which. As a finance manager, I practice this daily. Deductive reasoning is crucial for tasks bound by policy or a model—like applying a standard forecasting methodology to a new business proposal. Inductive reasoning, conversely, is the engine of strategic thought. It's used when we need to find new signals in the noise—like analyzing customer feedback to form a new hypothesis about pricing or feature development. The best decisions are rarely driven by one alone; they are a conversation between the logical rigor of deduction and the insightful leaps of induction. 🧭 What's a decision where your "gut instinct" and data were at odds? How did you reconcile them? #CriticalThinking #LogicalReasoning #DecisionMaking #BusinessStrategy #FinanceLeadership #LeadershipSkills #ProblemSolving #PostMBAReflections

🔗 Chain of Thought: The Hidden Superpower in AI and Human Reasoning 🧠✨ In both human and artificial intelligence, how we think often matters more than what we think. One powerful technique transforming reasoning in AI (and leveling up human decision-making) is called Chain of Thought (CoT) reasoning. 🧩 What is Chain of Thought? Chain of Thought is a step-by-step reasoning process, where instead of jumping straight to an answer, we break down the problem into smaller, logical steps. Think of it like showing your work in a math problem or walking someone through your thought process during a strategy meeting. This technique significantly improves accuracy, explainability, and compositional reasoning—especially for complex or multi-step problems. ⸻ 🛠️ How to Build It (for LLMs and People): 1. Prompt Engineering: For LLMs like GPT, use prompts like: “Let’s think step by step…” or “First…, then…, finally…” This nudges the model to reason explicitly. 2. Few-shot Examples: Include examples that demonstrate the reasoning path—not just inputs and answers. 3. Practice Decomposition: In human decision-making, get into the habit of breaking problems down and narrating your logic—helps with collaboration and clarity. ⸻ ⚙️ Where is it Used? 🔸 Agentic Workflows: In multi-step AI agents, CoT is essential for planning and task execution. For example: • An AI researcher planning an experiment. • A shopping assistant comparing prices, reviews, and delivery dates before suggesting an item. 🔸 Education & Tutoring: Tools like Khanmigo use CoT to help students learn by explaining each step of a solution. 🔸 Data Analysis & Decision Support: AI copilots that walk through reasoning—e.g., “Why did revenue drop in Q2?”—use CoT to explain their findings. 🔸 Creative Writing & Ideation: Even in storytelling, CoT helps agents generate coherent plotlines with character motivations. ⸻ 🧠 Why It Matters CoT is what makes AI feel thoughtful rather than robotic. It enables: • Trust and transparency • Better debugging of LLM errors • Complex task chaining and autonomy ⸻ 💡 Whether you’re training models, building products, or just trying to be a better thinker—Chain of Thought is a skill worth mastering. Curious how it can fit into your product or workflow? Happy to brainstorm! #AI #ChainOfThought #LLM #AgenticAI #PromptEngineering #DataScience #AIProduct #LLMAgents #Reasoning #AIExplained

How I Improved My Logic-Building to Land Offers at Amazon and Google When I started problem-solving, my approach was straightforward: open a platform like LeetCode, read a question, struggle for hours, and then watch the solution. But even after watching the solution, I often felt like I didn’t fully understand it. It made me pause and reflect: Why was I stuck? The answer was clear—I hadn’t built a strong foundation. So, I decided to take a step back and rebuild my approach from the ground up. Here’s what worked for me: 1️⃣ 𝐌𝐚𝐬𝐭𝐞𝐫𝐢𝐧𝐠 𝐭𝐡𝐞 𝐅𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐬 I focused on understanding the core concepts—data structures, algorithms, and patterns. Special attention went to areas like graphs, dynamic programming, sliding window, and trees. Mastering these gave me the framework to tackle problems systematically. 2️⃣ 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐢𝐧𝐠 𝐭𝐡𝐞 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 When revisiting problems, I didn’t just memorize the solution. I asked myself: Why this data structure? Why this pattern? I also challenged myself to think of alternative approaches. This mindset deepened my understanding and helped me adapt to different problem-solving scenarios. 3️⃣ 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐲 𝐰𝐢𝐭𝐡 𝐑𝐚𝐧𝐝𝐨𝐦 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬 To keep things interesting, I started solving random problems daily on platforms like LeetCode. This exposed me to diverse problem types and prepared me for unexpected challenges during interviews or contests. The journey wasn’t easy, and the results didn’t come overnight. But by focusing on the fundamentals, questioning every approach, and staying consistent, I gradually improved my logic-building skills. If you’re facing similar struggles, don’t hesitate to take a step back, work on your foundation, and stay consistent with practice. The results will follow! #ProblemSolving #LogicBuilding #DSA #Google #Amazon #Faang #CodingJourney #LeetCode #GrowthMindset

"Have You Exhibited Critical Thinking Today?" Each day, I question myself, "Have I demonstrated Critical Thinking today?" In my view, critical thinking is one of the most essential skills in life. It is a topic frequently discussed, but what is its true meaning? What is its real importance, and how can one apply it? 1. What does critical thinking mean? (For me) "A thoughtful process that evaluates and determines the validity and implications of information by grounding judgments in logical reasoning and evidence (facts)." 2. The Importance? The World Economic Forum champions critical thinking as an essential skill for our rapidly transforming future. It's not alone in this recognition: -The OECD's PISA emphasizes the need for robust critical thinking in our interconnected world. -McKinsey underscores the increasing demand for higher cognitive skills. -Top institutions like Harvard and Stanford champion critical thinking as foundational in their curricula. 3. How to Apply It? To apply critical thinking, one needs to demonstrate: -> Skepticism: Challenge the given (not just accepting it blindly). -> Logical Reasoning: Using logic to connect ideas. -> Open-mindedness: Welcoming alternative viewpoints and perspectives. -> Evidence-based Judgment: Basing conclusions on factual evidence. -> Self-awareness: Recognizing one's biases. -> Analytical Skills: Breaking complex issues into smaller, manageable components. -> Continuous Learning: Maintaining an attitude of continuous inquiry. What's your take on this? I'd love to hear your thoughts in the comments. If this perspective resonates with you, I'd be delighted to connect! #AsepTamar #StructuredThinking #ProblemSolving #StructuredPresentation

What’s the most important skill for a Data Analyst? Half of my life, I work as a data analyst. The other half, as a data scientist. When I wear the data analyst cap, I lean more into creative thinking. When I wear the data scientist cap, I focus more on technical depth. Just 2 weeks ago, I helped a company uncover and address a ₦78 million revenue decline. No—I didn’t build a fancy dashboard. I didn’t write a complex SQL query either. What I did was: ✔️ Understand the problem ✔️ Identify the right data ✔️ Match the pieces together That’s it Because without critical thinking, even the most advanced technical skills will fall apart. So how do you develop critical thinking as a data analyst? 📌 Ask Better Questions Train your mind to go beyond the obvious: ✔️ Instead of “What’s the result?”, ask “Why did this happen?” ✔️Use the 5 Whys technique to dig deeper into root causes. 📌 Challenge Assumptions Don’t take data or instructions at face value: ✔️ “What am I assuming?” ✔️ “Is there another perspective?” ✔️ “What evidence supports or contradicts this?” 📌 Practice Structured Thinking Break problems into parts: ✔️ Define the problem clearly ✔️ List possible causes/solutions ✔️ Evaluate based on logic and evidence 📌 Read Widely and Critically Expose yourself to diverse perspectives. Some top reads: ✔️ Thinking, Fast and Slow – Daniel Kahneman ✔️ The Art of Thinking Clearly – Rolf Dobelli 📌 Collaborate and Debate Join communities and participate in webinars like the Zion Tech Hub biweekly webinars. Explain your thought process. Defend it with logic. The truth is, AI is catching up fast. ChatGPT can build dashboards. Copilot can generate code and even models with the right prompt. So what makes us special? ✔️✔️ Our ability to think. ✔️✔️ Our ability to reason through ambiguity. Because let’s be honest, most stakeholders don’t even know what they want.. That’s where we come in, not just as analysts, but as critical thinkers who bridge data and decision. What do you think? Let’s talk. Kindly repost if this resonates with you. P.S: In 90-days you could become a Certified Data Analytics Professional, From UNIZIK Business School (UBS), Apply Now to be a part of that transformative learning experience https://lnkd.in/dbyzsHEE

Yesterday I produced a logical argument to reason why an algorithm was correct. How did I do that? Here are some logical thinking tips for algorithmic problem solving: - For each line, reason about the syntax and expected behavior for the calls and expressions. This mostly takes some language knowledge, API usage knowledge, and attention to detail. - If conditional statement occurs, employ the proof by cases strategy. Exhaust each possible case to ensure the code produces a correct output. - If iteration occurs or recursion occurs, employ an inductive proof strategy. Loop invariants can be used as a mechanism for achieving this with iteration. For recursion, raw induction helps. - If you’re dealing with a recursive data structure such as a tree or graph, structural induction helps. Otherwise, use all known facts about your domain to help reason about the correctess. In yesterday’s problem we were dealing with sums, so, as it turns out, facts about sums helped. As you can probably guess by now, if you have the logical thinking tools and knowledge base about your domain, you’ll likely be able to solve the problem. To solve, iterate over what you know about your domain and ask yourself if it can help you link it to what you don’t know and need to solve. Problem solving is about exploring the frontier of your knowledge graph and finding a pathway to a solution. Problem solving is path finding. #softwareengineering