The post An Intuitive Explanation of Bayes's Theorem explains that using Bayesian reasoning we can much better see how likely something is (by calculating conditional probabilities), and thus update our own beliefs as efficiently as possible.
However, the essay shows us how to calculate those probabilities using lots of math (duh!). Which is fine if you just want to grasp the concept, or need to evaluate a medical study or something.

But what about applying it in our daily lives? Nobody has time for divisions while evaluating evidence...
The article gives 2 hints to help. The first using odds ratios, the second using decibels. But odds ratios keep getting multiplied and thus quickly get unwieldy, while decibels would be great, if not for the fact that I have no clue what the log10 values of different ratios are, and I really dislike the thought of memorizing them.

I think I can offer a more intuitive way to approach this. The key insight is: We don't need exact probabilities. In daily life, we are not in the position of a doctor calculating precise likelihoods from study data. We are taking gut level probabilities and want to continue reasoning with the results. So all we need are ballpark numbers anyway. We want to know whether something is 8% or 80% likely. The exact percentages are just noise though, our intuition isn't that precise.

I propose to encode the likelihood information using duobels, rather than decibels. Log2 instead of log10. Then every change of that number by one halves or doubles one side of the odds ratio.
The table for that would look like this:

duobel	odds ratio	ratio	%
-2	1:4	1/5	20
-1	1:2	1/3	33
0	1:1	1/2	50
+1	2:1	2/3	67
+2	4:1	4/5	80

(For in between values, we can use n.6 (.585) to get a x1.5 instead of a x2 increase: 0.6 is 3:2, -0.6 is 2:3, 1.6 is 3:1, and so on.
n.2 is 1.15, n.4 is 1.3, n.8 is 1.75 -> this is in most cases already too precise for daily needs)

All we need to remember to calculate the ratios in our head is this:

0 is 1:1, even odds, a coin-flip
positive numbers have odds $2^{n}$ :1, negative numbers have odds 1: $2^{| n |}$
the odds ratio x:y is equal to the division $\frac{x}{x + y}$

That's it, we can translate any duobel number into an odds ratio or a ratio! And percentages for stuff like 2/3 we have already memorized, if we even need percentages in the first place.

Now let's talk likelihood shifts, where we can take advantage of the logarithmic measure of likelin...

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