Probability of Normal Distribution

In summary, the conversation discussed a problem involving test scores on a standardized test with a mean of 55 and a variance of 64. The question asked for the probability of a random sample of 256 scores having a sample mean score between 53 and 57. The solution involved using the z-score formula and calculating the probability using the standard normal distribution table. The final answer was 1, indicating a high probability of the sample mean falling within the given range due to the large sample size. The conversation also touched on the challenges and importance of intuition in solving statistics and probability problems.
  • #1
needhelp83
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Test scores on a standardized test have mean 55 and variance 64. What is the probability that a random sample of 256 scores will have a sample mean score between 53 and 57

I attempted the problem and came up with this:
u=55 and sd = 8 (Square root of 64)
[tex]\mu=55 \sigma=\sqrt{64}=8[/tex]
[tex]P(53< \bar{X} < 57) = P(\frac{53-55}{\frac{8}{\sqrt{256}}}<Z<\frac{57-55}{\frac{8}{\sqrt{256}}})=P(-4<Z<4)[/tex]

What am I doing wrong?
 
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  • #2
Your work looks fine to me. Why do you think there's something wrong?
 
  • #3
I felt like it was off because 4 is off the charts, so I figured I managed to perform a miscalculation somewhere
 
  • #4
Yeah, I know the feeling. These statistics and probability problems can really test your intuition quite a bit.
 
  • #5
[tex]\Phi(4)-\Phi(-4) = 1 - 0 =1[/tex]

So is this correct?
 
  • #6
Yes, to the accuracy of four decimal places. The large sample size really narrows down the uncertainty in the mean.
 
  • #7
Ok, thanks for the explanation. That makes sense
 

FAQ: Probability of Normal Distribution

What is a normal distribution?

A normal distribution is a probability distribution that is often used to describe the distribution of a continuous variable. It is characterized by a symmetric, bell-shaped curve and is commonly referred to as a "bell curve". It is important in statistics because many natural phenomena follow a normal distribution.

What is the mean and standard deviation in a normal distribution?

The mean, represented by μ, is the central value of a normal distribution. It is the point where the curve reaches its peak. The standard deviation, represented by σ, measures the spread of the data around the mean. It tells us how much variation there is in the data.

How is the probability of a normal distribution calculated?

The probability of a normal distribution can be calculated using the standard normal distribution table or by using a statistical software. The area under the curve represents the probability of a certain event occurring. For example, the probability of a value falling within one standard deviation of the mean is approximately 68%, and the probability of a value falling within two standard deviations of the mean is approximately 95%.

What is the central limit theorem and its relation to normal distribution?

The central limit theorem states that when independent random variables are added, their sum tends towards a normal distribution even if the original variables themselves are not normally distributed. This is why the normal distribution is often used in statistical inference, as it allows us to make assumptions and draw conclusions about a population based on a sample.

Can a normal distribution be used for all types of data?

No, a normal distribution is not appropriate for all types of data. It is most commonly used for continuous variables that are normally distributed, meaning that the data is evenly spread out around the mean. If the data is skewed or has outliers, a normal distribution may not accurately represent the data and a different probability distribution should be used.

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