Pranesh

“Avoid succumbing to the gambler’s fallacy or the base rate fallacy. Anecdotal evidence and correlations you see in data are good hypothesis generators, but correlation does not imply causation—you still need to rely on well-designed experiments to draw strong conclusions. Look for tried-and-true experimental designs, such as randomized controlled experiments or A/B testing, that show statistical significance. The normal distribution is particularly useful in experimental analysis due to the central limit theorem. Recall that in a normal distribution, about 68 percent of values fall within one standard deviation, and 95 percent within two. Any isolated experiment can result in a false positive or a false negative and can also be biased by myriad factors, most commonly selection bias, response bias, and survivorship bias. Replication increases confidence in results, so start by looking for a systematic review and/or meta-analysis when researching an area.”
― Gabriel Weinberg, Super Thinking: The Big Book of Mental Models

“Supervised learning algorithms typically require stationary features. The reason is that we need to map a previously unseen (unlabeled) observation to a collection of labeled examples, and infer from them the label of that new observation. If the features are not stationary, we cannot map the new observation to a large number of known examples. But stationary does not ensure predictive power. Stationarity is a necessary, non-sufficient condition for the high performance of an ML algorithm. The problem is, there is a trade-off between stationarity and memory. We can always make a series more stationary through differentiation, but it will be at the cost of erasing some memory, which will defeat the forecasting purpose of the ML algorithm.”
― Marcos Lopez de Prado, Advances in Financial Machine Learning

“It’s simple math, you know. Classic bell curve. Normal distribution. Most people are going to pile up in the middle, not going anywhere, but there’ll be a handful of outliers that go high and low, often for inexplicable reasons. Point is, even survivors don’t really understand why they survive—they just do.”
― Peter Cawdron, Losing Mars

“The first [method] I might speak about is simplification. Suppose that you are given a problem to solve, I don't care what kind of problem-a machine to design, or a physical theory to develop, or a mathematical theorem to prove or something of that kind-probably a very powerful approach to this is to attempt to eliminate everything from the problem except the essentials; that is, cut is down to size. Almost every problem that you come across is befuddled with all kinds of extraneous data of one sort or another; and if you can bring this problem down into the main issues, you can see more clearly what you are trying to do an perhaps find a solution. Now in so doing you may have stripped away the problem you're after. You may have simplified it to the point that it doesn't even resemble the problem that you started with; but very often if you can solve this simple problem, you can add refinements to the solution of this until you get back to the solution of the one you started with.”
― Claude Shannon