What Would Prove You Wrong? The Most Important Question In Investing
How treating your investment ideas like scientific hypotheses can protect you from costly mistakes
Sometimes I like to begin these articles with a thought experiment. So, if you’re willing, indulge me for a moment.
Let’s imagine two investors initiating a position in the same stock.
One writes a detailed thesis and diligently searches for news to confirm it. The other frames a hypothesis (you know, a tentative idea) and from day one defines the specific evidence that would prove him wrong.
Months later, a dreaded disappointing earnings call arrives. The first investor dismisses it as “just one bad quarter.” The second, having pre-defined this outcome as a thesis breaker, exits the position before the losses deepen.
In investing, as in science, how you test your ideas can make all the difference. Treating each investment like a scientific experiment (yes, the whole thing with falsifiable hypotheses and clear criteria for failure), can improve decision-making and protect us from our own biases.
It forces us to confront the question: “What would it take for me to change my mind?”
Answering that question ahead of time is powerful. It demands intellectual honesty and helps guard against our all-too-human tendency to see what we want to see.
So today, I’ll explore how applying the scientific method to investing leads to better outcomes, more objective judgment, and ultimately, a more robust investment process.
A Scientific Mindset for Investing
Investing is often described as an art, guided by experience, intuition, and sometimes gut feel. Yet there’s an argument to be made of seeing investing as an applied science. After all, both scientists and good investors are trying to discern truth from noise.
They key to the scientific mindset is htat it insists on testable ideas. In science, a hypothesis must be framed such that it can be proven wrong (a principle philosopher Karl Popper famously called falsifiability). According to Popper, a theory that can explain everything after the fact but predicts nothing concrete ahead of time isn’t truly scientific1.
This concept of falsifiability translates surprisingly well to hte investing profession. The key point for investors is that making your theories testable exposes truths you might otherwise miss.
Falsifiable You Say…
Everyone is familiar with an investment thesis: essentially, your rationale for why a security is a good buy.
Traditionally, a thesis is something you set out to prove – you gather supporting evidence, build a case, and hope the market agrees with you. The danger is that a thesis can become a pet belief. Once you’ve convinced yourself “Company XYZ will dominate its industry because of its innovative product”, confirmation bias creeps in.
We naturally start noticing only the news that supports our narrative and ignore anything that contradicts it. This is the opposite of scientific thinking.
The key to using this mental model is flipping the script: treat it as a hypothesis to disprove. In practice, this means designing your thesis to be falsifiable. Write down not just why “Company XYZ will succeed,” but also what evidence would indicate it’s failing.
A few weeks ago I wrote an article on how taking notes could help improving the investment process. Particularly, I believe Freenoting could be a useful tool in helping us device these hypothesis.
For instance, you might hypothesize: “Company X’s revenue will grow 20%+ annually over the next three years, driven by its new product. I will consider this hypothesis invalid if quarterly revenue growth falls below 10% or if a major competitor captures more than 15% market share.” That’s pretty specific, it sets quantitative benchmarks and a timeframe. It provides clear conditions that would prove the idea wrong. You are practically building a refutation checklist into your thesis.
Just one clarification here.
Not all falsifiable criteria need to be purely quantitative as in that example. It can also stem from observable, qualitative characteristics of the business – attributes that might not yet show up in the financials but are visible in how the company operates.
Think pricing power, customer loyalty, distribution leverage or speed of iteration, or talent density. While these are harder to pin down with a precise metric, they’re often visible in actions: how frequently a company raises prices without churn, how sticky customers are despite alternatives, how quickly new products ship, or whether top engineers are choosing to work there. Even anecdotal evidence, such as churn visible in app reviews, or rising Glassdoor complaints2, can serve as early refutation signals.
The key is to define what you’re observing and what would indicate a meaningful reversal. Qualitative signals can often precede quantitative deterioration.
This approach forces discipline. By articulating in advance what bad news or trends will throw cold water on your optimism, you make it easier to take off the rose-colored glasses later.
You’ve pre-committed to being objective.
A traditional thesis statement says, “Here is why I’m right.” A hypothesis says, “I think I’m right, but I’ll know I’m wrong if X, Y, or Z happens.” It’s a subtle shift in language that has a profound impact on your mindset. With a falsifiable thesis, negative information becomes critical data that tells you to re-examine or exit.
Just to clarify the concept a little further, find some examples below for different types of companies:
In the second column, you’ve set measurable tripwires. The predefined criteria help counteract the natural impulse to invent excuses like “Well, it’s just a seasonal slowdown, I’ll give it another quarter.”
Instead, you treat your original thesis as being invalidated unless new evidence truly overturns the negative trend. This doesn’t mean you must automatically sell after one bad report, but you are compelled to review the position with fresh eyes rather than doubling down in blind faith.
Look, I don’t know who’s reading this—maybe you already do all of this. But knowing it is one thing; doing it is another. Actively searching for disconfirming evidence is uncomfortable, and shifting our mindset to embrace that discomfort takes real effort.
In essence, you become your own devil’s advocate, just as a scientist would challenge his or her own theory.
How Can You Actually Apply This Mindset?
So, once you have a hypothesis-oriented thesis, how do you test it? In a laboratory, you’d run controlled experiments. Investing doesn’t allow for perfect lab conditions, you can’t hold all else equal while you tweak one variable. Nevertheless, there are few things you can do to take advantage of the mindset, let’s review a few of them:
Making the hypothesis explicit: this almost goes without saying, and you may already be doing it—at least implicitly—but listing the hypotheses in a spreadsheet or table, along with the rationale behind each one, is tremendously helpful. It clarifies what you're looking for in every new piece of news related to the company’s performance. For example, last week I published a write-up on Moury Construct SA (MOUR). Granted, it operates in a cyclical industry, with significant uncertainty across its various market segments and demand drivers. So, which are some of the falsifiable hypothesis in that case? Let’s see.
Again, these are just a few of the hypotheses that need to hold for the thesis to be valid. The value of this approach is that it forces you to be precise and transparent about what you're really betting on.
Starting with a small position: Rather than going all-in, you might buy a small position to observe how the thesis progresses. This is akin to a scientist doing a small-scale trial. If early results confirm your hypothesis, you can add more to the position – scaling up the “experiment.”
Historical backtesting: You can look at what happened in the past under the same or similar conditions. The classic example: if your hypothesis is that the business is recession-proof, then obviously check how it performed during past crises. Just keep in mind—a backtest is like an experiment run on a single historical path. It offers only one sample of evidence. Still, it’s a valuable tool to see whether your idea would have been repeatedly refuted by history or not.
Premortems: Coined by psychologist Gary Klein and popularized in investing by Michael Mauboussin, a premortem asks you to imagine ahead of time that your investment has failed spectacularly and to brainstorm plausible reasons why3. Assume your hypothesis was wrong – what likely killed it?
If you are using this approach you are less likely to be blindsided because you are actively probing your ideas. As Li Lu put it, “Investing is about intellectual honesty. You want to know what you know – and, mostly, what you don’t know.” 4
A falsifiable thesis framework is a powerful check on confirmation bias. By definition, if you’ve listed out the potential disconfirming evidence in advance, you’ve made a pact with yourself not to ignore it.
Munger often cited Darwin as a role model for intellectual honesty. Darwin trained himself to write down any fact that contradicted his theory as soon as he noticed it, knowing that his mind would try to “forget” inconvenient details if he didn’t record them5, remember: you get more of what you reinforce. As investors, we can adopt the same approach: when a news item or data point introduces even the slightest doubt, make note of it immediately and revisit your thesis. Don’t rationalize it away in the moment.
On Why Carrying a Notebook Might Make You a Better Investor
I have an old-fashioned belief that I only should expect to make money in things that I understand. I don’t mean understand what the product does. I mean understand what the economics of the business are likely to look like ten years from now.
Accelerating The Feedback Loop
If an investment hypothesis doesn’t pan out as expected, that experience enriches your understanding. You’ve learned something about what doesn’t work or about how a certain factor truly affects a company or market.
Soros famously said, “I’m only rich because I know when I’m wrong… I basically have survived by recognizing my mistakes.” In other words, it was the ability to quickly admit and correct errors, to shorten the feedback loop.
A falsifiable thesis accelerates this learning cycle. It gives you a systematic way to detect mistakes early. Rather than holding an underperforming investment while telling yourself “it’ll come back, I know I’m right,” you have a pre-defined point at which you’ll re-evaluate or exit.
Taking a small loss sooner is better than suffering a large loss later.
More importantly, it frees up mental energy to deploy into new hypotheses that might do better.
I’ve written before about other (and related ways) of accelerating feedback loops below:
How to Improve Forecasting Ability Using Findings in Meteorological Science
It was April 1950 and the United States Department of Commerce published the Monthly Weather Review with an article that would forever change the way accuracy of weather forecasts is measured.
A Stop-loss On Your Conviction
This process also guards against the sunk cost fallacy.
With a falsifiable thesis, you’ve effectively set a stop-loss on your conviction. You’re less likely to keep throwing good money after bad because you’ve decided in advance what “bad” looks like.
If that criterion is met, it’s a cue to move on without agonizing over pride or ego. It transforms the investment game into a series of trials where agility and open-mindedness win over stubborn conviction.
Paradoxically, having a rigorous sell discipline and willingness to be wrong can give you more confidence to let your winners run.
Why?
Because you know you’ve done the homework and set guardrails. If the position is still in your portfolio, by definition it hasn’t tripped any of your major alarms yet. You can therefore hold with conviction until the facts truly change, rather than getting shaken out by every minor fluctuation.
Conclusion
Thinking of each investment as a set of hypothesis to be tested can profoundly improve your process. By using this approach, you create a feedback loop to validate or refute your ideas in real time.
Of course, an applied science approach to investing doesn’t guarantee success and, hey, I’m not the first one to propose these ideas. There will always be an element of art and intuition in investing, especially when it comes to qualitative factors that are hard to quantify.
What’s clear, however, is that the very act of approaching your craft more scientifically will make you a more deliberate and self-aware investor.
So the next time you’re building a thesis on a company, pause and ask: What would convince me that I’m dead wrong?
You might be surprised how liberating it is to have an answer. It could save you from a disaster or give you even greater confidence to let your winner run. In either case, you’ve shifted the goal from being right to getting it right. And that, my friend, is a hallmark of both good science and good investing.
PS: Trying something new here. If you restack this post to help boost its visibility, I’ll send you a PDF with a list of “Excuses That Kill Objectivity: A List of Mental Traps.”
Disclaimer
This newsletter (the “Publication”) is provided solely for informational and educational purposes and does not constitute an offer, solicitation, or recommendation to buy, sell, or hold any security or other financial instrument, nor should it be interpreted as legal, tax, accounting, or investment advice. Readers should perform their own independent research and consult with qualified professionals before making any financial decisions. The information herein is derived from sources believed to be reliable but is not guaranteed to be accurate, complete, or current, and it may be subject to change without notice. Any forward-looking statements or projections are inherently uncertain and may differ materially from actual results due to various risks and uncertainties. Investing involves significant risk, including the potential loss of principal, and past performance is not indicative of future results. The author(s) may or may not hold positions in the securities discussed. Neither the author(s) nor the publisher, affiliates, directors, officers, employees, or agents shall be liable for any direct, indirect, incidental, consequential, or punitive damages arising from the use of, or reliance on, this Publication.
Popper, K. R. (1959). The Logic of Scientific Discovery. Hutchinson & Co. (Original work published 1934).
Researching Glassdoor, Trustpilot, Reddit reviews, or even Google reviews—and observing how they evolve over time—has served me well in the past and helped me avoid costly mistakes.
Mauboussin, M. J. (2009). Think Twice: Harnessing the Power of Counterintuition. Harvard Business Press.
Hopkins, J. (2017, November 8). Li Lu – Investing Is About Intellectual Honesty. Know What You Don’t Know. The Acquirer’s Multiple.
Munger, C. (2007). Poor Charlie’s Almanack: The Wit and Wisdom of Charles T. Munger (Expanded 3rd Ed.). Donning Company Publishers. (See commentary on Darwin’s habits, pp. 56–58).
That’s one strategy but not a good one if you’re looking for multi baggers, you’d be selling way too soon.
This is a great post. I think having a premortem is so important.