1. Bayesian Thinking

“Violent Crime Doubles”

If you read that headline in your local newspaper, you might assume you need to start watching your back.

But is that true?

Let’s say the risk of getting assaulted in your town last year was 1 in 10,000.

Since violent crime has doubled, the risk of assault is now 2 in 10,000.

In other words, the likelihood of getting assaulted is no longer 0.01% but 0.02%.

So, while the risk has doubled, it shouldn’t make you too concerned.

This example illustrates the big idea behind what statisticians call Bayes’ theorem¹; that we need to view new data in the light of previous events.

That’s not something that comes naturally to us.

We all tend to blow new information out of proportion.

So, whenever you learn something that seems extraordinary, try to put it in its proper historical context.

That way, your understanding of it will become more nuanced, accurate, and useful.

2. First Principles Thinking

First principles thinking² is a technique where you break complicated problems into their foundational elements.

It’s helpful whenever you want to find new solutions.

Imagine, for instance, that you’re struggling to find the time to work out.

You know from experience that your busy schedule makes it near impossible to get to the gym.

If you get stuck at that assumption, you might not work out for a long time.

But if you apply first principles thinking, you’ll find that the underlying fundamental principle here is progressive overload.

All you have to do to increase your fitness is to work out at a slightly more challenging level than your body is used to.

And you can do that with a quick high-intensity interval training routine at home.

It’s a simple example, but it demonstrates the point.

If you break down problems into their foundational elements, you can let go of previous assumptions and open your mind to new solutions.

3. Occam’s Razor

Occam’s razor³ is a problem-solving principle that can be summarised as:

“The simplest explanation is probably correct.”

It’s a helpful tool whenever you make initial conclusions with limited information.

Imagine, for example, that you come home and find your living room window open.

There are two possible explanations for this:

1. You had a lot on your mind when you left and forgot to close it.
2. Someone has broken into your home while you were away.

The first explanation only requires a little mindlessness on your part.

The second explanation, however, requires someone to have broken in, disarmed your alarm, cleaned up after them, and left without your neighbours noticing.

Since the first explanation is simplest, it’s the most likely to be the correct one.

Obviously, Occam’s razor isn’t perfect.

There are exceptions to every rule, and you should never follow them blindly.

But, as a rule of thumb, it can help you reach the correct conclusions faster and more often.

Footnotes

1. Bayes’ Theorem
2. First Principle
3. Occam’s Razor

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