Tis the season for predictions. If one has an audience one seem compelled to make predictions. You are better off reading the book Superforecasting than this article. The book explains in depth the simplest elements you need in making predictions and forecast.
It starts with – Base Rate – which is how frequent does the said event happen in general relative to all other events. For example
- What percentage of tweets are retweeted?
- What percentage of people are named Bill?
- What percentage of startups achieve $1B valuation?
- What are the chances of you winning Survivor when you start the season with 19 others?
The next step is an iterative process that refined this prior knowledge by seeking new information and refining your estimate. That is the posterior probability.
Most likely you won’t read the book, so I present here these two concepts set to the tune of Megan Trainor’s song.
Because you know I’m all about that base-rate
‘Bout that base-rate, no tails
I’m all about that base-rate
‘Bout that base-rate, no tails
I’m all about that base-rate
‘Bout that base-rate, no tails
I’m all about that base-rate
‘Bout that base-rate… base-rate… base-rate… base-rate
Yeah, it’s pretty clear, I ain’t no sigma two
But I can predict it, predict it, like I’m supposed to do
‘Cause I got that Bayesian that all the gurus chase
And all the right tunables in all the right places
I see the magazine workin’ that Crystalball
We know that shit ain’t real, come on now, make it stop
If you got logic, logic, just raise ’em up
‘Cause every inch of you is curious from the bottom to the top
Yeah, my mama she told me “don’t worry about your data size”
(Shoo wop wop, sha-ooh wop wop)
She says, “Bayesians like a little more posterior to hold at night”
(That booty, uh, that booty booty)
You know I am wont to be stick figure xkcd comic doll
So if that what you’re into, then go ‘head and move along
Iterative Path 
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