A mental model in predicting outcomes by relying on the law of large numbers which predicts that as the number of trials increases, the results will converge towards expected values.
The Law of Large Numbers (LLN) is a theorem developed by probability theory that states that the results obtained from performing an experiment a large number of times would be close to the expected value, and will become closer as more trials are performed. This concept is key in making informed predictions in fields like statistics, physics, computer science, and finance. To illustrate, when you flip a coin, your chance of getting either heads or tails is 50-50. However, if you flip the coin ten times, you might not get a perfect split of 5 heads and 5 tails due to chance. But if you were to flip that coin thousands, millions, or even billions of times, you should expect to find that the ratio of heads to tails converges towards 50-50. This is LLN in action where longer sequences portray a more accurate average.
"Remember that if the law of large numbers is on your side, you can play the game many times and the statistics will drive the outcome to the expected average." - Ray Dalio
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