Statistics with confidence intervals

In summary, the problem involves determining the necessary sample size to estimate the true mean porosity within a desired margin of error of 0.25, with 99% confidence. This can be calculated using the formula n = (2*z*σ/w)^2, where z = 2.575 for a 99% confidence level and w = 0.25/2 = 0.125. Therefore, a sample size of n = 138 is necessary to achieve the desired level of accuracy.
  • #1
war485
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Homework Statement



Suppose the porosity (in %) of samples taken from the ground found to be normally distributed with σ = 0.85 %

What sample size is necessary to estimate the true mean porosity to within 0.25
with 99% confidence?

Homework Equations



C.I. = confidence interval = mean +- z*σ*n^(-0.5)
n = (2*z*σ/w)^2
interval width = w
z = 2.575

The Attempt at a Solution



Not really sure what is meant by "... to within 0.25" maybe someone can help clarify this? Is it referring to the confidence interval width? Also, I thought that the confidence intervals do not estimate the true mean. I thought C.I. only estimates whether or not the other samples will have the same mean within the C.I. range.
 
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  • #2
Usually "to within xxx" refers to the desired margin of error - this would not be the length of the confidence interval, but half the length.

A confidence interval provides a range of values which can be considered "reasonable values" for the true mean (that is highly non-mathematical language, but I think it gets the point across)
 
  • #3
thanks for clarifying the problem statdad.
 
  • #4
0.25 is the interval width ("w"), which is the same as error margin. This can be interpreted as ±0.25. Since it was not stated as ±0.25 but as 0.25, they probably meant ±0.125.

The 99% C.I. implies a 0.5% probability under either tail, as statdad suggested. You should verify that your z value is consistent with that probability.
 
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Related to Statistics with confidence intervals

1. What are confidence intervals in statistics?

Confidence intervals in statistics are a range of values that are likely to contain the true population parameter with a certain level of probability. They are used to estimate the true value of a population parameter based on a sample of data.

2. How are confidence intervals calculated?

Confidence intervals are calculated using the sample data, the desired level of confidence, and the appropriate statistical formula. The most common method is to use the sample mean and standard deviation to calculate the confidence interval for the population mean.

3. What does the confidence level represent in a confidence interval?

The confidence level in a confidence interval represents the percentage of times that the true population parameter will fall within the calculated interval. For example, a 95% confidence level means that if we were to take multiple samples and calculate confidence intervals for each, 95% of those intervals would contain the true population parameter.

4. How do confidence intervals help with statistical analysis?

Confidence intervals provide a range of values that are likely to contain the true population parameter. This helps with statistical analysis by providing a more accurate estimate of the population parameter and allowing for a better understanding of the variability in the data. They also help to determine the statistical significance of results and make more informed decisions based on the data.

5. Can confidence intervals be used for all types of data?

Confidence intervals can be used for most types of data, including numerical and categorical data. However, the method of calculating the confidence interval may vary depending on the type of data and the population parameter of interest. It is important to choose the appropriate statistical test and formula for the specific data being analyzed.

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