Ep 233 | Do You Have Survivorship Bias?

Thank you for joining me today. It's Brian Gubernick, and welcome to the No Days Off podcast, where we believe that every single day brings a new opportunity to get better. Let's get into it. Today I want to talk about survivorship bias, and I want to leverage this, this survivorship bias concept to make us better decision makers, better thinkers, individuals that could process information a bit differently. And as a result, chart different courses of action that are superior to maybe what we would have done previously. Now, I think quite a bit around mental models. I'll tell you right now, it's, it's top of mind for me because my poor Charlie's Almanac book, the Charlie Munger a book that was recently released, uh, finally arrived. I was on backorder for a while. And why does that matter? Well, Charlie Munger, if you know anything about him, I mean, we've talked about him a ton here at No Days Off. I'm sure the No Days Off nation is fully aware of our buddy Charlie, who unfortunately passed away not too long ago. Charlie is the king of mental models. He's the king of leveraging different tools to make better decisions, especially around investment. And so that's very much top of mind as I've been reading his book. And I came across this survivorship bias and have been studying it a bit in and using it when analyzing success and failure, particularly in the real estate industry, the real estate business game. Now, what is this survivorship bias? Survivorship bias is a cognitive bias that occurs when we focus on the individuals or the things that have survived a process while overlooking those that did not because they're not as visible or, or they're not included in the, in the data. This sort of bias can lead to rather inaccurate conclusions and it, and it can make us more optimistic or give us an optimistic perception of success, an overly optimistic perception of success. This term is used in a number of different fields, especially in business and finance and historical analysis. And it really is meant to caution us against drawing conclusions based only on the things that we can observe, the observable successes, rather than consider— considering all things, the full data set, including of course failures. Let me give you a better example. Let's say in finance you look at, um, I don't know, a hedge fund, hedge fund performance. So suppose an investment firm analyzes the returns of all of the existing hedge funds to determine what makes for a successful fund. If this investment analysis firm, if this firm is only considering active funds and ignores all those funds that have either closed or just underperformed, maybe due to bankruptcy or poor management or whatever it may be, then they may actually draw inaccurate conclusions about the factors contributing to success. Now, the reality is the surviving funds in this scenario, they may have certain characteristics that have allowed them to persist, but those characteristics might not necessarily be indicative of future success. Ignoring the funds that failed could lead to a very skewed perception of what factors are crucial for success in the industry. Make sense? So let me give you a more specific example. Let's go back to the late 1990s, early 2000s, when we had a dot-com boom. And a subsequent bust. During the dot-com boom, many internet-based companies experienced insane growth and incredible success. Some of these companies like eBay, like Amazon, they're still, you know, very much thriving today. Now, if an investor or an analyst were to study the characteristics of all of these successful dot-com companies by only looking at the survivors like eBay and Amazon, They might identify certain traits or strategies that seem to correlate to success. Not they might, they would see certain stra— uh, traits or strategies that would correlate to success. However, many dot-com companies failed during the bust. They faced bankruptcy. They had significant financial challenges. And these failures are oftentimes overlooked in analysis that focuses only on the survivors. Again, this is the survivorship bias. So you end up potentially with misleading conclusions because you neglected the companies that did not, not survive. A real-life example would be Pets.com. Now, many of our listeners are probably too young to remember any of this, but Pets.com was incredibly well known during this particular era, late '90s, early 2000s. The company went public in 2000, became massive, and then was one of the most iconic casualties of the dot-com bust. Despite all of their marketing, tremendous marketing efforts, and everything that they had done to get to that point, Pets.com faced challenges in its business model, and they eventually shut down shortly thereafter. If an analysis only considered the successful survivors, it would miss all the factors that led to Pets.com's failure. So the key takeaway here is that the survivorship basis in this particular— I'm sorry, bias in this particular context involves focusing on the successes of the dot-com era without considering all the failures, which can lead to an incomplete understanding of the factors that contributed to success and will misguide you with respect to the decisions, investment decisions to make. Apply this in your industry. Apply this in your world. I spend a tremendous amount of time in the real estate industry. I analyze real estate performance specifically within big real estate teams. I even recently did a— about a year ago, did my own study where I called 29 of the highest performing teams around the nation. I have no idea why it was 29. I just decided to stop at 29. I actually interviewed 29 of the highest performing teams around the nation. And I had a definition for high performance. It had to do with profitability. It had to do with reputation. It had to do with length of time in the business and growth. And I had a number of different factors I was considering. And I interviewed these 29 and I came up with a report that I was able to share with my peers around, I think it was the 7 or 8 maybe it was 9 different things that all of these teams shared. Things like, uh, things like a specific approach to their, their daily routine, types of lead generation they were doing, ways they recruited talent, how they manage their reputation. Like, I saw a lot of consistencies amongst these 29 top teams. Now, if I'm being honest with myself and looking at that analysis that I did, while I, I found that to be really good information, and those I've shared it with have found it to be really good information. What it didn't actually include— and if I go back in time, I would revise it or revise my approach— what it didn't include was an analysis of those teams that frankly failed during that same time frame. So I looked at all the teams that have succeeded, and I drew conclusions around what led to success. But what would have made that analysis so much more powerful was to invert it and say, okay, while all these teams succeeded, now let's go find all the reasons why teams failed during the same time frame. In doing so, I would have had a more comprehensive list as to the steps necessary to not just survive but thrive. So what I'm trying to say is, in hindsight, I now see that my research project was incomplete. The conclusions it, it, it had drawn, which I think were really good ones, could have been a little bit more detailed. Could— there could have been some things that just I didn't see because I didn't do the analysis. In other words, I fell victim to the survivorship bias. So the thought for you today, the focusing question for you today, each and every day. I think all of you listening to the No Days Off podcast, you're aiming to get better. And we, and we on this podcast, we are studying those that have come before us and have had success in life, in health, in relationships, in business. And there's definitely a bias to lean into those individuals, their thoughts, and draw from them and apply that knowledge in our world. But I do think we can all not just look at the survivors or the winners, but also we should be analyzing those that did not have that success and understand what went wrong. Right? So it's almost that inversion technique. Instead of looking at what will lead to success, instead say, what will lead to failure? And then obviously chart a different course that is different than those that led to failure. You follow me? So where in your life right now is this survivorship bias showing up? Where are you studying success or who are you aiming to be like or model that's winning, but does not take into consideration those that are now, you know, off the map because they, they, they didn't, they didn't succeed. Right? Just a really good thing to think around. Of course, this requires you to pause, reflect, have some thinking time, which is kind of a core concept to all we do here at No Days Off. That's all I have for you today. No Days Off podcast. Thank you so much for listening. 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