Method to the Madness

People often say that how we see the world is what shapes it. And yes, our view influences how we feel, what we believe and how we act. But there’s a flaw in that idea. Just because something feels true doesn’t mean it is. Our senses can mislead us. Our thoughts are often shaped more by habit, memory, or other people’s views than by anything we’ve properly examined. That old line about perception being reality might be comforting, but it doesn’t hold up when you start looking at what actually works. What we think we know is not always what’s there.

This shows up clearly in how many businesses operate. Decisions are often driven by gut feeling, past routines, or whatever narrative is already in motion. People who question things are seen as getting in the way, not going with the plan. But when instinct takes the wheel and anyone who challenges it gets shut down, what you have isn't good leadership, it's chaos with a schedule. That way of working might feel dynamic, but it’s still madness. And if left unchecked, it leads to confusion, failure, and wasted effort. The good news is that there’s a way to bring some method to the madness; one that doesn’t rely on guesswork or groupthink, but on something that helps you get closer to what’s actually going on.

That’s where the scientific method can help, not just in research labs but in day-to-day business decisions. At its core, the method involves four actions: observe, propose a hypothesis, test that hypothesis through experiments, and evaluate the results. These four steps create a structure that helps us look beyond surface impressions and get closer to what's actually happening. In business, the same process can guide strategic choices, product design, or performance reviews. It provides a way to separate wishful thinking from useful insight.

The first step is observation. In science, this means paying attention to what the world is doing, carefully and repeatedly. In business, it means noticing patterns in customer behaviour, performance metrics, or team dynamics. But even this stage can be distorted. Scientists don’t step into their work with empty heads. Their training, previous results, and current tools all shape what they notice. The same thing happens in organisations. Executives, founders, and investors don’t see with neutral eyes. They come in with expectations, and those expectations filter what they notice and what they miss.

That’s why the second step, forming a hypothesis, is so useful. A hypothesis is just a guess that can be tested. It turns an observation into a statement you can investigate. In business, this might sound like “users aren’t renewing their subscriptions because the sign-up process is too complicated.” That’s your testable idea. The value here is that the hypothesis can be wrong. It isn’t your belief or preference. It’s something you’re willing to examine and revise if the data points elsewhere.

Next comes experimentation and observation. This is the step where science looks for structure, not just stories. You try different versions. You measure outcomes. You see what happens when variables are changed. The same applies in business. You pilot a campaign. You A/B test a landing page. You launch in one region first. These are business experiments. And like any experiment, they work best when designed carefully and interpreted objectively.

That brings us to the fourth step: evaluation. What did the test show? What actually changed? This stage is where you learn. But here, too, we can get trapped by perception. Sometimes people treat the results of a trial or initiative as proof of their original view, regardless of what the numbers say. They bend the interpretation to protect a favourite plan. This happens in science too. It’s one of the reasons the method is needed in the first place. The loop of hypothesis and test only works if the evaluation stage is honest and willing to admit when something didn’t go as expected.

Scientific models are useful, but none are flawless. They help us explain things for now, until better explanations come along. They reflect our best understanding at a given moment, not some final answer. You could call them working theories, shaped by the information at hand, subject to change as more pieces of the puzzle turn up. The same is true of market forecasts, customer profiles, and strategic roadmaps. These are current practical tools, not eternal facts. In both science and business, treating a model as if it will always hold up is one of the fastest ways to lose touch with reality.

What counts as strong evidence often depends on what people already believe. There’s a long list of moments in science when popular opinion held more sway than the actual data. And the same happens in boardrooms and product launches. That’s why asking questions isn’t just a good habit in science, it’s a necessary part of the process. In business, boardrooms fall in love with ideas that are no longer useful. This is why the habit of asking questions isn’t optional. It’s the only way to avoid locking yourself into a plan that stopped working three quarters ago. Good decisions depend not only on what the evidence says but on whether the system we’re using to interpret that evidence still makes sense.

Let’s go back to those thinking habits that support the scientific method. These are not just abstract traits. They show up in daily decision-making. There’s the instinct to question familiar explanations. There’s the effort to rely on measurable results. There’s the drive to understand parts in relation to the whole. And there’s the awareness that events tend to have causes, even if they aren’t obvious right away. You’ll see these same habits in strong business leadership.

Scepticism

Scepticism tells us to hold back from jumping to conclusions. In science, it’s the impulse that demands evidence. It keeps claims in check and stops shaky ideas from becoming dogma. But even scepticism leans in certain directions. We tend to doubt unfamiliar ideas more than familiar ones. We question fringe theories while accepting mainstream views, even when the evidence for both might be equally fragile. Our trust in certain systems often overrides our doubts.

In business, scepticism isn't just a habit, it’s an asset. Questioning what's widely accepted can lead to innovation, better strategies or spotting risks others miss. It means not blindly trusting what's worked in the past. Businesses that last tend to be the ones willing to ask awkward questions, even when everything seems to be working.

Empiricism

Empiricism is the habit of relying on observation and measurement. It’s the idea that we should only believe what can be tested, verified or detected. This works well in research. But once we move beyond controlled conditions, into messy realities like consumer behaviour or market volatility, things become harder to pin down. We build stories around data. But the data doesn’t always lead to one obvious story.

Even when results are clear, their meaning can shift. Two analysts might look at the same sales figures and draw completely different conclusions. That’s not dishonesty. That’s interpretation, coloured by background, incentives and pressure. In business as in science, data can be misread, cherry-picked or twisted to fit a narrative. Which is why empirical thinking, staying close to what the data actually shows, and staying aware of how we might be bending it, is a useful discipline for anyone making high-stakes decisions.

Reductionism

Reductionism is the idea that you can understand a complex system by taking it apart and analysing its parts. This can work well in tightly controlled systems. But many real-world problems don’t reduce neatly. In science, breaking a brain down into neurons doesn’t explain thought. In business, breaking down a failed product launch into website clicks or survey scores doesn’t always explain why people didn’t buy.

Leaders often crave tidy answers. Find the cause. Fix it. Move on. But things like customer loyalty, employee motivation or cultural shifts are rarely so linear. Knowing when not to over-simplify is a real advantage. Sometimes, understanding the system means stepping back, not drilling down.

Determinism

Determinism holds that everything happens because of something that came before. In science, that might mean an outcome follows a chain of causes. In business, this turns up in narratives like: ‘sales are down because marketing spend dropped,’ or ‘the merger failed because the cultures didn’t align.’ These stories can be useful. But they can occasionally gloss over uncertainty, randomness or surprise.

Without common sense, deterministic thinking on its own can become a trap. It suggests that if we just identify the right cause, we can fix the outcome. But human behaviour, market swings and social change rarely follow such neat scripts. So it’s helpful to ask what led to a result, but it’s equally helpful to recognise when the chain of cause and effect has missing links.

The Scientific Method and the Null Hypothesis

The scientific method is often described as a loop: form a hypothesis, test it, analyse the results, adjust the hypothesis and repeat. In research, that helps avoid jumping to conclusions. In business, it helps you stay responsive. The ability to adapt, test again and change course is what separates organisations that grow from those that stall.

This means that the scientific method is iterative. You don’t just find the answer and stop. You test it again, revise it, and stay ready for surprises. That mindset is invaluable in business and personal growth. People and markets change. A career strategy that worked five years ago may no longer be relevant. The willingness to adjust your thinking is what keeps you from falling behind.

The process also leans heavily on the null hypothesis. This is the default position that nothing’s happening, nothing’s connected, no effect has been proven. It sets the bar. If someone claims that something works, the burden is on them to show that it does. Until then, the default stands. This doesn’t mean being cynical. It means not assuming until the evidence earns it. That’s a useful posture in business, especially when resources are limited and the temptation to act fast is strong.

For example, if someone claims that life exists elsewhere in the universe, the null hypothesis is that it doesn’t. This doesn’t mean scientists believe life is absent. It means they require strong evidence to say otherwise. The null hypothesis can’t be proved. You can only fail to reject it. Even if we search every known planet and find no life, we still can’t say life doesn’t exist somewhere we haven’t looked. But one confirmed case, microbial life on Mars, for instance, would be enough to reject the null.

This system builds caution into the method. But it also shows the limits. Some claims are easier to "disprove" than others. Some ideas, no matter how popular, rest on fragile assumptions that haven’t been properly tested. And sometimes the evidence needed to overturn a theory is ignored because it doesn’t fit. This is true for both science and business, but we'll look at a couple of scientific theories to demonstrate the point.

Current Theories and the Need for Open-Mindedness

The Big Bang theory suggests the universe started from a hot, dense state and expanded rapidly. It explains a lot: cosmic background radiation, the abundance of hydrogen and helium, the redshift of distant galaxies. But there are snags.

One major issue is entropy. According to the second law of thermodynamics, systems tend to move from order to disorder. But the Big Bang model suggests that, from chaos, stars and galaxies formed. Over time, matter organised itself into structures. This seems to run against the rule. How does a universe that starts in high entropy develop islands of low entropy, like planets, plants or people?

The model also faces the question of origin. If everything came from a singularity, where did the singularity come from? If it came from a cloud of particles, where did the cloud come from? The deeper you go, the fewer answers you find. The theory explains what happened after the bang. It struggles with what came before.

As another example, the theory that life arose from non-living matter , called abiogenesis , is widely discussed, but not well understood. Scientists have managed to form amino acids in lab conditions that simulate early Earth. But they haven’t created life. Not even a simple cell. The leap from chemistry to biology remains elusive.

This raises problems. Life is highly ordered. Cells require membranes, genetic code, energy systems, repair mechanisms. None of this has emerged spontaneously in any lab. The odds of such systems assembling by chance, even with billions of years and countless attempts, are difficult to calculate. And if life came from non-life, why haven’t we seen it happen again?

Again, I’m not suggesting these things didn’t happen. I’m saying we haven’t yet demonstrated how they did. The evidence is partial, the mechanisms are unclear and our best efforts so far haven’t crossed the key thresholds. It’s not unreasonable to keep looking at these as the most likely answer. But it is also fair to acknowledge how much we still don’t know.

We could go on, with theories like Dark Matter or Evolution etc. All of these are well established but continue to have unanswered questions and issues that demand we have an open mind, and continue to challenge.

And that’s the lesson. No matter how settled something seems, there’s value in staying open minded. In business, that might mean realising that your customer personas need updating. Or that your supply chain model is outdated. Or that your assumptions about what motivates your team no longer hold up. The world shifts. So should your view of it.

The Problem of Discarding Evidence

One of the more dangerous habits in both science and business is discarding data that doesn’t fit the story. When results match expectations (or our theories), they’re often welcomed. When they don’t, they might be ignored, buried or explained away. This isn’t always deliberate. It’s human nature. People like neat stories. Data that contradicts the dominant theory creates discomfort. It raises awkward questions. But if we only accept the evidence that fits, we don’t get a true picture of what’s happening. That's not the scientific method.

In research, ignoring inconvenient results skews the entire field. In business, it can mean doubling down on a product no one wants, because early metrics looked promising. It can mean clinging to a narrative about growth, when all the signs now point to decline. It can mean re-organising a department again believing that it will solve a problem rather than looking for the root cause (something familiar to anyone in the Public Sector). Recognising this pattern, and resisting it, is one of the most valuable things a leader can do.

The scientific method encourages us to follow the evidence, wherever it leads. But in practice, some paths are more popular than others. Theories that explain the most get favoured. Evidence that doesn’t fit is often considered noise, not signal. That can stop progress. In business, this might mean sticking with a failing strategy because the initial data once looked promising. In life, it might mean staying in a job or relationship that no longer fits because we’re invested in the story we've told ourselves. Recognising this tendency gives you a better shot at making honest assessments.

The scientific method, with its habits of doubt, observation, simplification and cause-seeking, gives us a way to explore those gaps. But it’s not immune to the problems of perception. Our biases shape our questions. Our beliefs shape our interpretations. Our fears shape what we ignore.

None of this means we should throw the method out. Far from it. It’s one of the best tools we have. And you don’t have to be a scientist to use this thinking. You just have to care about whether what you believe is still true. Good leadership means questioning the story, not just the stats. It means checking the tools you’re using to understand what’s going on. It means being prepared to update your view, even when it’s uncomfortable.

It's okay not to have all the answers. The best leaders, thinkers and doers aren’t those who know everything but the ones who are willing to think more carefully about what they believe, why they believe it, and how they might make better decisions by holding their views with both confidence and flexibility. That mindset pays off in any field. A mindset that brings method to the madness.