How to interpret the Standard Error in this experiment?

AI Thread Summary
The Standard Error (S.E.) of $38.72 indicates that one could expect to win or lose up to $77.44 (2x S.E.) approximately 95% of the time, assuming a normal distribution. Given the limited number of trials, calculating discrete binomial probabilities provides a more accurate assessment. The analysis shows a 94.6% probability of wins ranging from 6 to 15, translating to winnings between -$60 and +$75. Additionally, the probability of outcomes falling within ±2 S.E. is 98.1%. Understanding these probabilities is crucial for interpreting the results of the experiment effectively.
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Homework Statement
Let's say you play a shell game. There are three shells and one of them has a coin under it. If you pick the one with a coin under it you win $10. If you pick a shell without the coin, you lose $5. You play this game 30 times.
Relevant Equations
I know that the expected value of one trial is 0. The average is (10 - 5 - 5)/3 = 0. Even with 30 trials the expeccted value is going to be 0.

The S.D. of one trial is $7.07.
But the Standard Error of 30 trials is Sqrt(30) * 7.07 = 38.72
Does the S.E. of $38.72 mean, I sould expect to win or lose up to $77.4 (2x S.E), 95% of the time?
 
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That is assuming the probability distribution approximates to a normal distribution. Since the number of trials is not very big, you can be more accurate by calculating the discrete binomial probabilities. I've just done this, and there is a 94.6% probability of the number of wins being from 6 to 15 inclusive, i.e. the winnings being from -$60 to +$75 inclusive. (The probability of being between ±2SE is 98.1%)
 
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