Chad Grills recently wrote about Adam Grant's new book Originals. I haven't read the book yet, so I'll reserve judgment on that. However, I couldn't help but notice a few points Chad tries to make. He points out five lessons that he learnt from Originals. I'll give you an example.
The first lesson is that 'all in' is not always a sign of commitment. He takes the example of Warby Parker. Adam Grant was approached to invest in this company when it was starting up. He declined because the founders were still having other day jobs when they started off and he didn't think they were all in and hence lacked commitment and/or confidence. And they went on to become a billion dollar company.
It's not just Chad that does this. I have seen many people make arguments like this. Many many people. Everyone from politicians to scientists to social activists to entrepreneurs to middle managers. Including me. A lot of us take a statistical result and point out how following that didn't work out in a particular example.
Of course it didn't! That's how statistics works. It is the science of probability. After observing a large enough sample set, it allows for making predictions with defined confidence levels. In lay speak, after observing how things turned out in many similar scenarios, it is mathematically possible to predict what will happen in a similar event in the future. And this prediction will be right, say, 80 percent of the time. Or 90. Or 95. It doesn't matter. It is something less than 100.
Which means that there is a 20 percent or a 10 percent or a 5 percent chance that the prediction will be wrong. All this does is tell us how things will be a majority of the time. Which means, it is also telling us that there will be exceptions.
Statistical analysis abstracts reality. It doesn't care what happens in individual cases. It tells you that if you go to an IIM or an IIT, you'll get a well paid job. It tells you that if you dedicate your childhood to learning to play football, you will not become a Premier League start. It tells you that if you take public transport to work, there will be no reduction in the pollution levels. You get the idea. It just tells you the most probable outcome.
But we live in reality. We are always thinking, yes this happens to a lot of people, but not to me. My situation is different. And maybe it does turn out different. Maybe it doesn't. But when it turns out different, it doesn't mean that it is unwise to make decisions based on statistical analysis. It just means that you (or the environment around you) had the ability to change some variables that the statistical analysis assumed would be constant.
If you make a decision consistent with statistical analysis, it is like investing in an Internet startup or real estate, you are assured of a good return (kidding!). If you make a decision inconsistent with statistical analysis, then you stand to take blame. Because you didn't take a statistically significant decision, that becomes an easy scapegoat. But it was obvious things would work out fine if only you had made the right decision, people will say. Whereas, when things don't work out despite you making a statistically consistent decision, the poor chap did everything right, it was just bad luck, people will say.
To an outsider, there is no visibility into the reality of how things work in a specific case, like Adam Grant and Warby Parker. So, they generally make decisions that are on the safer side, which is the side of the highest probability.
But, when we make decisions for ourselves, we make it as insiders, where we have a lot more information than what the statistical frame of an outsider provides. So, it only acts as an alerting mechanism to tell us we need to be sure of why we are going with an option that has a seemingly lower probability of coming true.