How to interpret the Standard Error in this experiment?

In summary, The Standard Error is a measure of the variability or spread of the data in an experiment. It is calculated by dividing the standard deviation of the sample by the square root of the sample size and is used to estimate the precision and accuracy of the results. A large Standard Error indicates a high level of variability and can affect the Confidence Interval. It is important to interpret the Standard Error in an experiment in order to assess the reliability and accuracy of the results.
  • #1
musicgold
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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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  • #2
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%)
 

1. What is the standard error and why is it important in this experiment?

The standard error is a measure of the variability or uncertainty in the sample mean compared to the true population mean. It is important in this experiment because it helps us understand how much the sample mean may differ from the true population mean, and therefore how reliable our results are.

2. How is the standard error calculated?

The standard error is calculated by dividing the standard deviation of the sample by the square root of the sample size. This takes into account the variability of the data and the sample size, giving a more accurate estimate of the standard error.

3. What is the significance of a larger or smaller standard error?

A larger standard error indicates that there is more variability in the sample data, meaning that the sample mean is likely to be further from the true population mean. A smaller standard error indicates less variability and a more precise estimate of the true population mean.

4. How does the standard error relate to the confidence interval?

The standard error is used to calculate the confidence interval, which is a range of values that is likely to contain the true population mean. A smaller standard error will result in a narrower confidence interval, meaning that we are more confident in the accuracy of our results.

5. Can the standard error be used to determine the significance of the results?

No, the standard error alone cannot determine the significance of the results. It is important to also consider the sample size and the magnitude of the effect being measured. A larger sample size and a larger effect size will result in a smaller standard error, but this does not necessarily mean the results are more significant.

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