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Question 2
Question 2

Precalculus Module 5, Topic B, Lesson 10: Student
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Chapter 5: Discrete Probability Distributions
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... Theorem 2.2 (Weak Law of Large Numbers) If X1 , X2 , . . . , Xn are independent and identically distributed with a finite first moment and E(Xi ) = m < ∞, then X1 +X2n+···+Xn converges to m in probability as n → ∞. Theorem 2.3 (Strong Law of Large Numbers) If X1 , X2 , . . . , Xn are independent and ...
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Law of large numbers



In probability theory, the law of large numbers (LLN) is a theorem that describes the result of performing the same experiment a large number of times. According to the law, the average of the results obtained from a large number of trials should be close to the expected value, and will tend to become closer as more trials are performed.The LLN is important because it ""guarantees"" stable long-term results for the averages of some random events. For example, while a casino may lose money in a single spin of the roulette wheel, its earnings will tend towards a predictable percentage over a large number of spins. Any winning streak by a player will eventually be overcome by the parameters of the game. It is important to remember that the LLN only applies (as the name indicates) when a large number of observations are considered. There is no principle that a small number of observations will coincide with the expected value or that a streak of one value will immediately be ""balanced"" by the others (see the gambler's fallacy)
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