Probability problem gone horribly wrong

In summary, the conversation discusses the normal distributions of weight for men and women, with mean and standard deviation values given. The probability of a randomly chosen man weighing less than a randomly chosen woman is calculated using a dummy variable and the normal distribution table. The second part of the conversation discusses the probability of the average weight of a sample of 30 men being less than the average weight of a sample of 40 women, and the use of a different variance formula. The probability is found to be very small, indicating the need for significant deviations from the average weights in order for this scenario to occur.
  • #1
war485
92
0

Homework Statement



Weight: men --> mean = 75, standard deviation = 15
Weight: women ---> mean = 55, standard deviation = 11
both normally distributed
one woman and one man are randomly picked.

1) probability that the man weighs less than the woman

2) if there was a random sample of 30 men and 40 women from this population, find the probability that the average weight of the men is less than the average weight of the women

Homework Equations



standard deviation = square root of variance
z = (value - mean) / standard deviation
z (normal) table

The Attempt at a Solution



I'll try to make it as clear as possible here:

1) P(man < woman) so P(W-M > 0)
then I let a dummy variable X = W - M
so expected(X) = 55 - 75 = -20
variance(X) = 11^2 + 15^2 = 346
so P(X > 0) = 1 - P(X < 0) = 1 - P(Z < 1.0752) = 0.140

2) I thought this was pretty much the same as above but the variance calculation was different:
Expected(X) = -20
Var(X) = 11^2 / 40 + 15^2 / 30 = 10.525
so P(X > 0) = 1 - P(X<0) = 1 - P( Z < 20 / square root of (10.525) ) = 1 - P(z < 6.1648) and clearly, I can't get a z < 6.1648 for the normal distribution! It's not in the table, and it wouldn't make sense to have a probability of 0. Help!
 
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  • #2
Let <W> be the average weight of 40 women, and <M> the average weight of 30 men. Both are again normally distributed. Then consider X = <W> - <M> like in the first problem.
 
  • #3
I did that. Still getting same answer. Does the expected value change when you take a random sample from a population? Maybe I did my variance formula wrong? Is it n-1 instead of n?
 
  • #4
Ah, I see that is precisely what you did, I just couldn't see that right away because you all compiled it into one line.
I don't really see anything wrong, then.
It's not really weird that the probability is small (it is non-zero, but very small). I mean, for the average weight of the men to be less than that of the women, you would have to have the statistical oddity of picking significantly light (as in: a few standard deviations below average) men and/or significantly heavy (a few sigma above average) women. And not just 1 of them, but several.
 
  • #5
never had such a small answer before (pretty much close to zero) so I thought something was wrong. Thanks for checking. :D
 

FAQ: Probability problem gone horribly wrong

What is a probability problem gone horribly wrong?

A probability problem gone horribly wrong refers to a situation where the expected outcome or probability of an event does not match the actual outcome. This can occur due to errors in calculations, incorrect assumptions, or unexpected variables.

What are some examples of probability problems gone horribly wrong?

Some examples of probability problems gone horribly wrong include predicting the outcome of a coin toss, predicting the number of red M&M's in a bag, or predicting the probability of winning a game of chance. In these situations, the actual outcome may differ greatly from the expected outcome.

What are some common causes of probability problems going wrong?

Common causes of probability problems going wrong include human error, incorrect assumptions, lack of complete information, or unexpected variables that were not taken into account. Additionally, using faulty or outdated mathematical models can also lead to incorrect probabilities.

How can one prevent probability problems from going wrong?

To prevent probability problems from going wrong, it is important to double-check all calculations and assumptions, gather as much information as possible, and account for any potential variables. Using accurate and up-to-date mathematical models can also help in preventing errors.

What are the potential consequences of a probability problem going wrong?

The consequences of a probability problem going wrong can vary depending on the situation. In some cases, it may lead to financial losses, while in others it may have more serious consequences. For example, incorrect probability calculations in the medical field can lead to misdiagnosis or incorrect treatment plans.

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