Finding a Minumum N from Binomial Distribution

AI Thread Summary
To estimate the probability of Hershey's Kisses landing flat, one must conduct trials and apply the Binomial Probability Formula. The challenge lies in determining the minimum number of trials (N) needed for a result that is accurate to one decimal place. The Law of Large Numbers suggests that a sufficient number of trials will yield a reliable ratio. However, isolating N in the binomial formula is complex, and clarity on what constitutes "reasonably accurate" is essential. Conducting the experiment and analyzing the data will provide the necessary insights for this problem.
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Homework Statement



From the text: Use Hershey's Kisses to estimate the probability that when dropped, they land with the flat part lying on the floor. How many trials are necessary to get a result that appears to be reasonably accurate when rounded to the first decimal place?

Homework Equations





The Attempt at a Solution



Well assuming that I already obtained some ration through a numerous amount of trials ( by the Law of Large Numbers), how would I use that value to obtain a minimum N amount of trails necessary to get a reasonably accurate result?

I know that the Binomial Probability Formula is:

P(x) = \frac{n!}{(n-x)!x!} \bullet px \bullet qn-x

How would one isolate n in that formula though? Or should I approach this a different way?
 
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The wording of this problem implies that they expect you to actually do this experiment, using Hershey Kisses, then use the data from your experiment.
 
HallsofIvy said:
The wording of this problem implies that they expect you to actually do this experiment, using Hershey Kisses, then use the data from your experiment.

You will also need to decide what is meant by "appears to be" and "reasonably accurate". (These would be issues on which people can honestly disagree!)

RGV
 
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