Brain Food for Pioneering Spirits: How do we actually reach a conclusion — by starting from a general assumption and testing it, or by starting from specific observations and building a theory from there? Dr. Yasemin Yazan, guest speaker Mona, Marc, Sabine and Ria dig into deductive versus inductive thinking (with abductive thinking as a special case in between), moving from biased market-research surveys and Clubhouse’s botched feature rollout to Brexit rhetoric, math class, and why AI inherits our blind spots.
Note: This session was recorded live in German as part of the Deep Talk Club, so the video above is German-language audio only. What follows is an English translation of the conversation, lightly edited for readability — the back-and-forth between the moderator and participants is preserved, but the original timestamps and speaker names have been left out.
Today’s reasoning is about how we draw conclusions in the first place: deductive versus inductive thinking, with abductive thinking as a special branch in between. Deductive thinking works from general assumptions toward specific conclusions and corresponds to quantitative research methods; inductive thinking works from specific observations toward general conclusions and corresponds to qualitative research methods. Abductive thinking addresses the fact that truly assumption-free research is impossible — instead of pretending we have no prior assumptions, we make them transparent and check whether they actually hold. Guest speaker Mona, a former product manager turned entrepreneur, brings in vivid real-world examples: badly designed market-research questions that use the wrong language for the target audience, the tradeoffs between open and multiple-choice survey questions, and the cautionary tale of Clubhouse’s botched feature rollout. From there the room opens up into search-engine filter bubbles, AI bias, and why talking to other people is the fastest way to spot our own blind spots.
Note: This article is based on a spoken conversation from a Clubhouse room. The video below contains the full recording; below that you will find the entire conversation translated into English as continuous text.
Good morning, everyone. Today I’d like to start with a somewhat “heavier” topic, at least when it comes to the terminology: deductive and inductive thinking. I came across this via a source called “Study Master,” which draws a clear distinction between the two. Deductive thinking means reasoning from the general to the specific — you start with an assumption or a hypothesis and then check whether it holds. That corresponds to quantitative research methods. Inductive thinking means reasoning from the specific to the general — you start with observations and derive a hypothesis from them. That corresponds to qualitative research methods. And then there’s a third, more specialized branch: abductive thinking. It addresses something important — namely that it’s actually impossible to approach any topic completely free of prior assumptions. So instead of pretending we have none, abductive thinking is about making those prior assumptions transparent and then checking whether they’re actually valid. I wanted to bring this topic into the room today because I think it touches our everyday lives in ways we’re often not even aware of — whether that’s test formats, search terms we use, or technologies running in the background that pick up on what we type. And I’m very happy that Mona is with us to open the conversation, since she brought this topic in during an earlier, first approach to it as well.
Good morning, everyone, and thank you for having me. Yes, when I think about deductive versus inductive thinking, the first thing that comes to mind from my own experience is market research and survey design. Early in my business, I sent out a survey and got almost nothing back — empty responses, or answers that didn’t really tell me anything. When I looked closer, I realized I had written the questions using my own internal jargon instead of the language my target audience actually uses. If the people you’re surveying don’t recognize themselves in your wording, they simply can’t answer meaningfully, no matter how good your underlying question is. And that still happens to me now — just recently I got a market-research question that used the term “Shadowdancer,” and I genuinely couldn’t answer it in any useful way, because the term meant nothing to me in that context.
That ties directly into another tension in survey design: open questions versus multiple-choice questions. Open questions give you rich, honest answers, but they’re harder to analyze at scale and harder for respondents to answer quickly. Multiple-choice questions are fast and easy to evaluate, but they force people into categories that may not fit at all — you end up picking the “least wrong” answer rather than the true one. Both approaches have their place, but you have to be conscious of which one you’re choosing and why.
The example I keep coming back to, though, is Clubhouse itself. At one point the app pushed a major feature update overnight, without any prior consultation with users and without proper beta testing. Suddenly, workflows that people had relied on — like finding their own room again, or replaying a recorded voice message — simply broke. For me, that was a capital mistake in product development and change management. If you want to introduce something new, you consult your users first, you run it past beta testers, you gather feedback before you roll it out to everyone. Clubhouse did the opposite, and the backlash was significant. It’s a perfect example of what happens when a company operates purely from its own internal assumptions about what users want, instead of testing those assumptions against reality first.
Multiple-choice formats have the same blind spot in testing, by the way. I once took a Spanish placement test and scored quite well — purely by guessing my way through several multiple-choice questions I didn’t actually know the answer to. The format rewarded pattern-matching and elimination, not real knowledge. It’s a good reminder that the tool you choose to measure something shapes what you actually end up measuring.
The same issue shows up with search engines and social media. The algorithms behind them are built on assumptions about what we want to see, and those assumptions create filter bubbles — we increasingly only see content that confirms what we already believe. I’d really recommend the Netflix documentary “The Social Dilemma” on this; it goes into a lot of depth on exactly this mechanism. And it doesn’t stop with search engines — it applies to AI and tools like ChatGPT too. These systems are trained on human-generated data, and human-generated data already carries our biases. So the AI inevitably inherits them. Honestly, I think the fastest way to catch your own blind spots and biases is simply talking to other people — other perspectives surface the assumptions you didn’t even know you were making.
Thank you so much, Mona, that was wonderful. What becomes clear here is just how multifaceted this really is. And even if we don’t consciously work with the terms “deductive” or “inductive” in everyday life, these aspects accompany us in many places, often completely unconsciously — whether that’s test formats, the search terms we use, technologies running in the background, or algorithms picking up on our input, or some training course where we take a test or fill out a survey. In all of these situations, it’s genuinely valuable to think in advance about what you actually want to find out, and then to consider your approach in light of that goal — whether you’re proceeding deductively or inductively, and how you can best get there in order to draw the conclusions you’re actually after. That was my point at the start: fundamentally, this is about reasoning toward a conclusion. We split it into deductive as the quantitative approach, and inductive as the qualitative approach. Neither is inherently good or bad — we always have to look at the context and check what actually makes sense there, and how we can use each approach to derive meaningful results. Mona mentioned she has an appointment and will need to step out soon, and she has in fact already left the room. So I’d like to invite everyone else to join in and bring further practical examples or contexts — where does which approach fit, and what other impulses do you have on this topic? I’ll go on mute now and see who’d like to come up next. Good morning, dear Marc.
Before I start, I’d like to ask the audience to send Yasemin a little heart, and you’ll understand why in a second. Let’s do it together — one, two, three: Happy Birthday to you, Happy Birthday to you, Happy Birthday, dear Yasemin, Happy Birthday to you!
Oh, you’re so sweet, thank you, that’s wonderful. We didn’t rehearse that! Just let yourself be hugged, Yasemin. Ah, thank you so much, how lovely. I’m taking screenshots, this is so great. Thank you, thank you, thank you, I’m thrilled. And with GIFs too, that’s fantastic. Thank you, thank you. And I’m so glad I get to start my birthday here with you, with this reflection space as the opening. I’m curious what you all have to say about the topic too. At some point I thought, “oh no, did we really pick a ‘heavy’ topic for today of all days?” But there are no coincidences — things come to us for a reason, there must be some good purpose behind it. Marc, Sabine, would you like to pick up on the examples we opened up?
Sure, happy to. It was heavy material, I have to say. I could follow your introduction to some extent, but it went a bit over my head, because I’ve done very little scientific work in that sense. Still, I understood enough to catch myself in it — I often get surveys, for example from our insurance platform. A lot of the people answering those surveys deal with insurance every day, but I don’t, and yet I’m asked to respond. Sometimes I can answer from genuine conviction, but often the options offered just don’t fit, and there’s no “not applicable to me” option. The same thing happens with the economic survey from our chamber of commerce’s general assembly — a lot of respondents have foreign trade dealings, and there’s a whole section about that, but it doesn’t apply to me at all, and there’s still no box for “doesn’t apply.” I’d love to add a free-text field for that. But I also understand why they don’t — if you allow that, the surveys get too complex to evaluate. I think that’s roughly what you meant with the inductive side.
Wonderful, thank you so much. And as you were talking, more thoughts came to me. We can probably find examples like this everywhere in daily life. What you said about insurance made me think immediately of bureaucratic language — how often do we fill out official forms and think, “what does this term even mean?” Sometimes it’s vocabulary we simply can’t make sense of. Just a day or two ago I wanted to call BMW, and we all know that situation where you have to navigate some phone menu. I wanted to ask about ordering new tires, not just a tire change, and the menu offered me “sales,” “parts,” or “service.” I thought, well, this could fall under any of those — which one do I actually pick? Luckily there was a fourth option to just stay on the line and get connected to the main switchboard first, so I could ask directly which department I actually needed. That situation is one we probably run into all too often in everyday life. And underneath it is exactly this quantitative, deductive logic — you want to apply a pre-filter to route people to the right place quickly. But when the categories don’t clearly cover your case, as in this example, you’re stuck wondering what to pick. At least there was a fourth option here that gave me a way out. Often there isn’t — sometimes you only get to choose among three fixed options, and either everything fits or nothing does, and you still have to click something. You already suspect you’ll end up in the wrong place, but at least then you get a human on the phone who can redirect you. Marc, did you want to add something before we bring Ria in?
Yes, overall — I’d like to reflect for a moment. I had exactly the reaction Sabine described. Just hearing the terms “deductive” and “inductive” thinking gave me a knot in my head, to the point that I almost didn’t want to be in this room. I didn’t understand the explanations either, because that’s always the funny thing — when specialists explain things using words I also don’t understand, I end up with even more knots in my head. I hope I can reproduce this correctly: my girlfriend is a biology teacher preparing students for their final exams, and I need a very primitive explanation to grasp anything. So here’s my primitive version: deductive thinking is one or two examples used to reach a conclusion. Inductive thinking is many examples used to form an opinion or make a decision. I can work with that. And then I found, for myself, that a syllogism is an example of deductive thinking. What’s that, you ask? Thankfully whoever explained it to me — even though I didn’t understand most of it — gave an example, and I’d like to share it because it helped me a lot: “All humans are mortal. Socrates is a human. Therefore, Socrates is mortal.” All humans are mortal — that’s established, scientifically confirmed. Socrates is a human — also confirmed. Therefore, Socrates is mortal. One example, or a couple of them, and a conclusion follows.
Then I asked myself, is this useless knowledge or does it actually help me? And I realized: our brains have a short attention span. Which immediately made me think of economics and politics — and for me, deductive thinking is the perfect tool for steering people in a particular direction. Since you only have one message to deliver in thirty or forty seconds, it’s exactly that same structure: “All humans are mortal, Socrates is a human, therefore he’s mortal.” That’s how votes get won. My favorite example is Brexit. The British campaign said: “We spend 365 million pounds a week on the EU. If we put that into our own healthcare system instead, we’ll all be much better off.” A very simple example — and the real art is in what you leave out, because the underlying issue is far more complex. The skill is in dropping the important nuance and boiling it down to one example that feels conclusive to everyone: “yes, he’s right, that makes sense to me.” Short and punchy, to arrive at a decision, politically or economically. And I notice I use deductive thinking myself as an entrepreneur — on the phone I often only have fifty or sixty seconds, so I need one example so compelling that the other person immediately says, “I understand, he’s right, I’m with him.”
Let me pick up on that distinction between deductive and inductive again. When you take examples and derive conclusions from them, that’s the deductive space — that’s correct, and it’s the quantitative side. But quantitative doesn’t actually refer to the number of examples — it’s not about few versus many examples. Both few and many examples can be deductive. The moment I fall back on examples or on existing theses, I’m drawing on prior assumptions. I take something as an example and transfer it onto other contexts to derive a conclusion. With the qualitative, inductive side, the idea is that I either largely set aside my prior assumptions, or — as with the abductive approach we mentioned — I make them transparent, and try to approach the topic as openly as possible, without falling back on existing methods or examples, but instead starting with a genuinely open, blank-slate mindset to derive new insights. Say, for instance, I want to develop a new product but I don’t actually know what my market needs. If I make assumptions about what the market needs and then test those assumptions, I’m back in the deductive space — regardless of whether I offer four options or ten options to choose from. That’s still about a fixed, quantitative pre-selection. It’s always quantitative in the sense that it deals with fixed categories, so to speak. You can trace it this way in many situations: whenever a pre-selection has already been made, and something is meant to be derived from it, you’re drawing on set prior assumptions. Inductive thinking, on the other hand, is about not making those prior assumptions at all — choosing a topic and going into it completely open, asking, “what comes to mind about this?” That’s actually what we normally do on Clubhouse — we usually just set a room topic as the rule, though today we deviated a bit by adding more explanation and framing upfront, which already set a direction. But normally, only our room topic is fixed, and then we simply see what comes up. And very different things can come up. I still remember one room I’ll never forget — it was about “blue light,” and Sabine started talking, and I wondered, what does this have to do with the police, in air quotes? But it turned out to be about blue light from screens, which is what we actually wanted to discuss. That shows how something completely unexpected can surface, something we hadn’t considered at all, possibly totally different from our prior assumptions. In that moment, we’re in the inductive, qualitative space. The question then is whether it’s actually useful for what we’re trying to do with the room — whether we pick it up or not, which is itself a decision to be made. But first, it opens up the space to surface aspects we might never have thought of otherwise. And it’s a shame that in everyday life we very often act deductively and quantitatively, unconsciously, while the inductive, qualitative side gets short shrift in many places — even though it’s extremely valuable for this kind of reasoning-toward-a-conclusion. So it’s worth approaching and training that side too. And with that, I’d like to bring Ria in — I actually picked this topic partly because Ria brought these terms into our very first approach to the subject. Ria, maybe you could share, independent of whatever you already wanted to say, how this plays out at school — is it something that gets practiced there, or does it fall short too? I’d love to hear from your experience.
Good morning, everyone, and to you as well. I can join today because it’s my day off, it’s Wednesday. I’m completely confused, though — I missed the first half hour. What I’ve heard from you just now is not at all what I originally meant by these terms. I think I actually overwhelm my students with them. I try to use these two concepts to introduce certain topics in geography or social studies — something I throw at their feet, like globalization, which is a topic in ninth grade right now. I tell them: you have the chance to break this topic open for yourselves, completely independent of what’s in the textbook. And there are two different ways of thinking about it — the deductive approach and the inductive one. And of course they look at me like I’m speaking a foreign language, completely blank. So then I explain it in a much simpler way, tied to something from my own math background — specifically proof by induction. This might sound a bit much, but bear with me.
You all know square numbers — 1, 4, 9, so 2 times 2, 3 times 3, 4 times 4, and so on. That’s what’s called a series in mathematics. It’s relatively simple because we know how it works: we take the first number we know, 0 times 0 is 0, that’s the starting point. Then we add 1, getting to 1, and 1 squared is still 1. Then we add another 1 to get 2, and 2 times 2 is 4. So there’s a logical chain for how we get from one result to the next. Proof by induction shows how the first term arises, how you get from the first term to the second, and how the second and third results follow. From there, you can calculate the x-th, y-th, or millionth result using the formula. And the formula is proven by proving the first term, proving the step from the first to the second term, and then proving it holds for all further terms — that is, going from the small to the large, from the specific to the general. That’s my explanation for inductive thinking: I start from myself — what does globalization do to me, and what does that mean for my parents, for my city, for my country, for everyone? The scope keeps widening, and you try to use logical steps to reason from yourself to everyone. It’s complicated, there are plenty of pitfalls, and you can easily get it wrong, because you’re taking properties of one specific case and generalizing them to all cases.
Deductive means starting from the big picture — that’s how I explain it to my students — and then working your way down to increasingly smaller elements. What’s interesting is that deductive thinking is like starting from a model, say a globe, and saying, “roughly, this is how we can picture the Earth — here are the continents, Africa lies directly south of Europe, the Mediterranean is in between.” We can see all of that perfectly well. But what’s missing? The fact that the Earth isn’t perfectly round but slightly flattened. The mountains are missing. The actual scale — how high the mountains really are, how deep the water is, and so on — none of that can be shown, because it’s just a model. But once you zoom into a specific region — say, if you took a cross-section through South America, from the Atacama Trench to the Andes — you’d find an elevation difference of about sixteen kilometers: almost 10,000 meters of depth on one side, and the highest peak at nearly 6,000 meters on the other, nearly sixteen kilometers apart. You don’t see that on the globe — you only see it once you go into the details. In the details, you keep encountering more and more specific properties that matter for that particular region — tied to climate, water conditions, deserts, and so on — things that simply aren’t visible on the globe. That’s exactly where I agree with you completely. It’s a fairly simple explanation, and the kids then try to articulate it themselves — “we could do it this way, starting from myself, or this way, starting from the big picture, looking at banks overall and then checking what that means for people, or looking at the war in Ukraine in connection with globalization.” One is more deductive, the other more inductive. But the students suddenly recognize that you can take different directions when thinking, and that these different directions open up new horizons and new perspectives — because you start to see this interplay: you can’t always reason from yourself onto everyone, and you can’t always reason from everyone onto yourself. Logic plays a huge role here, because logic always gives you the ability to abstract, generalize, or simplify. And introducing children to these different fundamental ways of thinking, even just a little, can eventually give them a real understanding — and honestly, it wasn’t clear to me before that this also connects to how marketing and advertising use these very same approaches. That was my train of thought on why I brought this in. Thank you.
Wonderful, thank you so much. I think this also calls for another example or a bit more specificity, to make the distinction really clear, because “from the specific to the general” and “from the general to the specific” can be interpreted quite differently depending on context. The origin of that distinction, in the part of the conversation before you joined, Ria, was around research methods — how this gets applied there. If we take your math example with the formula, we can look at it from two directions: inductively and deductively. If I go about it inductively, that means I don’t have the formula yet, and I want to develop one. I take exactly the kind of example you described and derive a formula from it — we all remember this from school, plugging in various x’s and y’s until we move from numbers to a formula. That formula then becomes something we can always apply — a kind of rule, valid no matter which numbers we plug in later. In that case, we’ve derived something from the specific — that’s the inductive path — we took specific examples and derived a formula that holds generally for all sorts of cases. And to confirm that it really does hold generally, no matter which number you plug in, you test it and check whether it actually works. If it does, you end up with a formula that operates on an abstract level and is always valid. Conversely, in mathematics we also have formulas that clever people have already worked out for us. If I then simply apply an existing formula, I’m in the deductive space, because the formula has already been developed — I’m no longer testing the formula, I’m applying it. I’m drawing on knowledge someone else already developed, whether that’s a formula, a method, or anything else, and using it in everyday life to calculate something. That’s the deductive space. The downside can be that I don’t discover anything new, because I’m simply falling back on an existing formula and applying it.
Take change management as an example — we’ve talked about this in previous rooms. A company can develop certain fixed ways of thinking and operate entirely within them, which means certain aspects never even come into view — blind spots. Over time, the company has effectively derived its own “formulas” — often in the form of established rules, things that have simply proven themselves over time. Underlying that are certain cultural values, rules that developed gradually, a certain established way of handling things. And then, when a new employee joins the team, we make sure they learn those rules and handle things the same way. But sometimes a new employee asks: “why is it done this way? Why don’t we do it completely differently — wouldn’t that be much simpler?” And that often leads to irritation, or to the classic response: “that’s just how we’ve always done it.” But that’s exactly the other kind of thinking — questioning certain established areas again, approaching things differently, not just grabbing the existing formula or rulebook and applying it, but actually going back into review mode and asking, “does it really have to be this way, or could it be different?” And then we’re no longer in the deductive space, but in the inductive one. Suddenly we can derive new insights again, against the backdrop of blind spots that may have gone unnoticed. I don’t know if that helps — can you work with that?
Yes, I already gave a thumbs up. Especially that explanation with the formula — discover a formula, and it’s inductive. Use an existing formula, like the Pythagorean theorem, apply it, and you’re deductive. That’s such a simple, clear explanation, I love it. But it wasn’t clear to me before that these ways of thinking can also be applied so strongly in interpersonal contexts, to explain certain processes, and in marketing too. It’s another one of those wonderful “aha” moments I’m going to bring back into the classroom, and I’m really grateful to you both for that.
Wonderful, thank you so much, and I’m grateful that you planted this seed in our very first approach to the topic, so we could pick it back up and look at it in more depth today. I can absolutely understand that for some of you, as Marc said earlier, this creates a bit of a knot in the head — but it really is worth looking at more closely, taking different contexts and checking, again and again: is this actually inductive or deductive? I think it’s genuinely valuable to practice building your own formulas sometimes, not just applying existing ones, because this distinction carries real meaning in different areas of our everyday lives, even if we’re often not consciously aware of it. So I hope some of you had a few “aha” moments or new impulses to keep exploring on your own. Given the time, I’d like to move into the closing round now — Marc, Sabine, Ria, what is this room leaving you with, what would you like to add before we close?
I find this such an exciting topic. What really stands out to me is that inductive thinking, as I described earlier, is like a fire alarm for me — because when I make claims like “if you’re an entrepreneur, you need a good website, all good entrepreneurs have a website, and if you’re not that successful, maybe it’s your sales materials or your website” — that’s exactly the pattern. And deductive thinking, the way you explained it so well, dear Yasemin, is really about questioning everything, observing: is that actually true? We always tend to see what we want to see, but you can apply different thought patterns and arrive at new insights. That only works if you think outside the box and genuinely question things — and I’ve noticed in myself that my brain always wants a quick answer, and once something sounds plausible, I just follow that path. Catching myself in that moment and asking, “wait, is that actually true? Could I try something else?” — that really helps me. Thank you for the room, and I wish you a wonderful day and lots of lovely connections. Thank you, Sabine.
Interesting — I can’t quite follow all of it, Maria, and I hope you’re not going to walk into school tomorrow announcing “today we’re starting to think inductively or deductively.” I see how it applies in science, and what you said about what one “should” do makes sense to me, but I don’t know that I’d label my own thinking “inductive” or “deductive” tomorrow. Still, thank you for the great topic, Yasemin, and I wish you a truly wonderful birthday — not just in business, but in life too. Take care.
In any case — thank you so much. Ria?
I already gave my closing thoughts earlier. I found it very interesting and I’m glad I got to join. As my very last word, I just want to say: thank you, Yasemin, and I wish you a wonderful day.
Wonderful, thank you all so much. A heavy topic for a birthday, in a way, but a very valuable one, I think, and I’m glad we got into this exchange. It was also lovely to have Mona with us at the start. Maybe it really did plant a seed for some of you — simply realizing that there are two approaches, two directions you can take when thinking: whether you start from a given assumption and build on it, or whether you try to set that assumption aside and approach the topic as openly as possible to see what insights emerge. That’s the simple version, and remembering exactly which is which — deductive or inductive — certainly isn’t always easy, and honestly, it doesn’t have to be. Just find whatever anchor point works as a bridge for you. What matters most is recognizing that there are two directions of thinking, and that it’s worth observing, in everyday life, which one shows up where, and which direction it’s taking — and maybe reflecting on how it could have gone differently, and what value that might have created. That might be a good starting point. We had lots of examples today showing how much this accompanies us in everyday life, completely unconsciously, without us ever really noticing. And we always say education is an important foundation — the moment we create awareness, we direct our focus there too. So I’m confident that in the days after this room, you’ll run into situations again where you can consciously ask yourselves: which direction of thinking is this, actually? And that alone already makes a difference. I’m curious to see what settles and grows from this for you.
And with that, let me mention tomorrow’s announcement, which I’ve already teased a few times: we have an expert talk tomorrow with Julien Backhaus on solution-oriented thinking. I’m really looking forward to it — if you have the time and interest, please join us, it’s sure to be another very interesting room. I’m curious to see which aspects we’ll get into and what Julien will bring in from his own perspective, experience, and expertise. So it’s worth tuning in if your schedule allows. As for me, aside from Deep Talk Club, I’ve actually kept today open — nothing concrete planned, we’ll just see spontaneously where the day takes us. I’m looking forward to it. And I hope to welcome you all back here tomorrow, same time, 8 a.m., for Deep Talk. With that, I wish you all a wonderful day. Bye for now, ciao, ciao.
