![]() The thing is, all you know is that this tree is 4 years old. Therefore, like a good (and slightly sadistic) data scientist, your mind instantly moves towards trying to predict its death. Neither do you, but you do know that they are notoriously famous for one thing - dying. ![]() They clearly know nothing about Bonsai trees. It didn’t even come with a care manual or an instruction booklet. You’ve been gifted a Bonsai tree from your strange Uncle, in what was clearly a panic-bought Christmas present. Predicting lifetimes of Bonsai trees - the Copernican principle The following was inspired by a chapter in “ Algorithms To Live By ” by Brian Christian and Tom Griffiths. I’m not promising to predict the future, I’m simply going to show you the best techniques we have to do so, which often produce surprisingly good results despite our lack of data. In this article, I’ll discuss briefly some great insights from Statistics that will help you answer such questions. For example, what is the probability that a Bonsai tree you’ve been gifted for Christmas will make it to the awkward family gathering? ![]() ![]() Or simply you will know the distribution of a population and nothing more. Often in life, you’ll have to predict things with little or no data. ![]()
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