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The law of large numbers states that the average of many trials converges to the expected value, but it does not imply that future trials will compensate for past deviations. The gambler's fallacy is the mistaken belief that if early trials deviate from the mean, later trials must produce opposite outcomes to 'balance out' the average immediately.

Conditions: The speaker is contrasting a common intuition with the actual meaning of the theorem.; Trials are independent.

The law of large numbers states that the average of many trials converges to the expected value, but it does not imply that future trials will compensate for past deviations. The gambler's fallacy is the mistaken belief that if early trials deviate from the mean, later trials must produce opposite outcomes to 'balance out' the average immediately.

Conditions: The speaker is contrasting a common intuition with the actual meaning of the theorem.; Trials are independent.

The sample mean converges to 50 because 50 is the expected value $E(X)$ of the random variable $X$ (number of heads in 100 fair-coin tosses). According to the law of large numbers, as the number of trials $n$ approaches infinity, the sample mean $\overline{X}_n$ approaches the expected value.

Conditions: The coin is fair.; $X$ counts heads after 100 tosses.; $n$ approaches infinity.

The sample mean converges to 50 because 50 is the expected value $E(X)$ of the random variable $X$ (number of heads in 100 fair-coin tosses). According to the law of large numbers, as the number of trials $n$ approaches infinity, the sample mean $\overline{X}_n$ approaches the expected value.

Conditions: The coin is fair.; $X$ counts heads after 100 tosses.; $n$ approaches infinity.