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The scientific method is an empirical method of discovering the truth of how the world works. It has been codified as a truth-seeking mechanism that evolved over millennia, with major contributions from Aristotle, Francis Bacon, René Descartes, Isaac Newton, Karl Popper, and many others.
The scientific method involves careful observation coupled with rigorous scepticism, because cognitive assumptions can distort the interpretation of the observation. Scientific inquiry includes creating a hypothesis through inductive reasoning, testing it through experiments and statistical analysis, and adjusting or discarding the hypothesis based on the results.
The world is very complex and multivariate. Phenomena can have a multitude of different causes. We cannot just guess or assume scientific explanations, the human mind is not equipped for that. Intuition is not a good enough instrument to discover the causes and underlying principles of the nature of reality. Scientific truths can be counterintuitive, uncomfortable, unbelievable. Our own prejudices and beliefs have absolutely no bearing on the truth, but often distort our view. Consequently, we (humanity) had to develop a systematic process of exploring the range of different potential explanations, testing them one at a time, and provisionally accepting those that seem plausible, while approaching the search for truth with a large dose of scepticism and doubt.
The search for (scientific) knowledge can be likened to a murder investigation. Suppose a murder takes place in a large city, and there are no clear suspects. To catch the perpetrator, you have to cast a wide net, filter it down to some suspects (”hypotheses”), and investigate them individually, giving them due process. When human nature is unrestrained, emotions and prejudice infect the process and you end up with mob rule, like the witch-burning in Medieval Europe and lynching of African-Americans in the 19th and 20th centuries.
The scientific method and the legal system are similar in the sense that both are codified methods for discovering the truth through systematic searching and testing. Crucially, they restrain the human impulse towards prejudicial and biased judgement. There are undoubtedly impartial judges and dogmatic scientists, but processes have been codified and applied in those institutions that lead, collectively, to good judgements.
The case for having a scientific mindset is just as strong in business as it is in science and law. A lot of books have come out in recent years based on this premise. Books like “Think again” and “Thinking, fast and slow”. But they miss the mark. They focus a lot on cognitive biases, and (I suppose) encourage the reader to memorize cognitive biases, as if they will recall them as they’re about to err in a judgement and catch themselves in the heat of the moment. That approach underestimates how deeply ingrained cognitive biases are in human nature. Instead, what you need is a new way of thinking and a process that is somewhat immune to (nearly irradicable) cognitive biases. We propose a combination of first principles thinking and the scientific method (or rather, a stripped down version of the scientific method, given that business is faster moving and therefore demands that we balance speed and precision).
The case for using the scientific method in business is that good decision making requires judging the situation correctly and choosing the best action to take, given the situation. And, like in science and law, the challenge with judging correctly is that the world is complex and a wide range of possibilities and interpretations may be correct, and none of them are obvious! Intuition is not a good guide, and cognitive biases cloud the judgement. Yes, business decision making is usually easier than understanding the natural world through science, but the influence of cognitive biases is much, much greater (confirmation bias, appeal to authority, groupthink, etc.)
The scientific method has proven itself beyond doubt (look at all the technology around you - the computer you’re reading this on, for starters), but is it really used in business? Yes, in fact, the world’s best companies, and particularly startups, use the scientific method.
McKinsey, the most elite strategy consulting firm in the world which takes on the hardest business problems in the world when their clients are stumped, is big on hypothesis-driven problem solving. This allows them to rigorously analyze the root causes of difficult business problems and come up with a good solution with a degree of certainty that is not possible if you do it willy-nilly.
In the startup world, the scientific method is embedded in the highly influential Lean Startup methodology. The Lean Startup Methodology emphasizes formulating hypotheses about business models and market demands, experimenting to test these hypotheses, and learning from the feedback gathered during these experiments. This approach allows startups to develop products and strategies iteratively, continually refining and improving them based on real-world data rather than assumptions. By adopting a cycle of build-measure-learn, businesses can quickly identify what works and what doesn't, pivoting when necessary to adapt to changing conditions and customer needs.
A study from Bocconi University took 116 Italian startups, split them into two groups, held a course for them on entrepreneurship, but gave the treatment group additional training in the scientific method. They followed up both groups for one year and measured the outcomes. The scientific thinking group had earned an average of 7900 euros in the first year, while the control group had earned an average of 900 euros.
So, how can businesses apply the scientific method in a way that is efficient but also leads to good, precise decision making? Logic trees!
A logic tree is a graphical breakdown of a problem that dissects it into its component pieces in a logical way. It is a map of the problem which you work on systematically. Logic trees are the main work tool of the “consulting approach to problem solving” used at McKinsey and other top management consulting firms. (I will not go into great detail in this article - you can read more about this here).
The two main types of logic trees are issue trees and hypothesis trees.
Issue trees are used to break down a problem into causes of or solution to the problem, while hypothesis trees are used to test one particular solution or claim, by breaking it down into what needs to be true for that solution/claim/hypothesis to hold true. To compare it again to science, issue trees are good for exploring the entire problem/solution space and coming up with hypotheses, while hypothesis trees are good for testing hypotheses rigorously.
In theory, if you make an issue tree that follows the MECE principle, i.e. every split is mutually exclusive and collectively exhaustive, you explore every possible issue - absolutely nothing is left out.
Take this example of a simplified issue tree showing how Elon Musk might have been thinking when he came up with the idea for the Boring Company, a tunnel-building company that wants to dig tunnels and funnel traffic underground in cities with a lot of traffic.
A textbook example of a MECE split, and the split that led to discovering this idea, is splitting the issue of how to increase road capacity for cars into increasing capacity above ground, on ground, or below ground. These three sub-issues are non-overlapping and fully comprehensive - there is nowhere else traffic can go other than those three places. So, issue trees are a good way to explore all possible solutions in a first principles way, i.e. independent of conventional thought, or what seems reasonable. MECE splits should be comprehensive, so you explore all solutions. Some of them might be nonsensical, but sometimes you discover great solutions.
Elon Musk famously thinks from “first principles” - i.e. he questions all assumptions and reasons up from principles he establishes to be true by himself, instead of relying on conventional wisdom. He may or may not not use issue trees, but it’s clear from the way he talks that this is his mode of thinking. If you want to understand better how Musk thinks, I recommend this article and this article.
After coming up with the initial idea for the Boring Company, Elon might think of it like a hypothesis (”this is a good idea and would actually work”) and test that hypothesis rigorously. In that case, he would use a hypothesis tree.
The hypothesis tree might look something like this (simplified):
Now the problem is structured as a set of testable sub-hypotheses. Crucially, you structure the reasoning first and then collect the data to either prove or disprove the hypothesis.
Using a hypothesis tree triggers a mindset shift from confirmation (trying to prove the hypothesis in spite of the facts and moving the goalpost when you find contradictory evidence) to investigation.
To fully apply the scientific method, there are many skills and principles you need to learn. But this mindset shift (from confirmation to investigation) is the essence, and using logic trees to achieve that is a clean 80/20 solution - i.e. you get 80% of the impact with 20% of the effort.
In conclusion, the scientific method provides a robust framework for navigating the complexities of business decision-making. By adopting a systematic, hypothesis-driven approach, businesses can mitigate cognitive biases, make data-driven decisions, and foster a culture of continuous improvement and innovation.
To learn more about how you can use logic trees in your business and establish a culture of scientific inquiry where the best ideas are brought forward and win, feel free to click around on our website, read some blog posts, and contact us for a demo!
The truth is rarely pure and never simple.
Oscar Wilde
The great tragedy of science — the slaying of a beautiful hypothesis by an ugly fact.
T.H. Huxley
Because it is so unbelievable, the truth often escapes being known.
Heraclitus
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