Determining a confidence interval for data.

In summary, to get a 95% confidence interval for the mean surface roughness of the oil pipes, the formula x ± t-val(s/√n) can be used with t-val being t19. This can be done even if the data is not normally distributed, as long as the sample size is greater than 30. However, some experts suggest that a sample size of 20 may also be acceptable if the data is not heavily skewed.
  • #1
NewtonianAlch
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Homework Statement


I have 20 data points of the surface roughness of oil pipes, the mean, and the std of the sample.

I need to get a 95% confidence interval of the mean surface roughness of these pipes. I did a boxplot, and found that it's negatively skewed, and therefore not normally distributed:

32234.jpg


Would using the t-table be OK? I'm usually used to doing this for data when it is normally distributed, I'm not too sure what to do when it's not normal.

This is what I did:

x ± t-val(s/√n)

Where the t-val is t19 and I looked up the t-table for the value. s = std and n = 20 and x is of course the mean.
 
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  • #2
when the sample size is greater than 30 you can use the t-confidence interval even if the population is not normal.
 
  • #3
several of my professors have said n=30 is a number that's been around for a while and is used as a rule of thumb, but you don't have to follow it super strictly. How skewed is the data? If it's not horribly skewed, if it's even vaguely bell-shaped, I'd imagine n=20 is acceptable. But please correct me if I'm wrong, I'm still new at this.
 
  • #4
I suppose n=20 is reasonable for using t-intervals
 

What is a confidence interval?

A confidence interval is a range of values that is likely to contain the true population parameter, with a specified level of confidence. It is used to estimate the precision of a sample statistic and to quantify the uncertainty in the estimate.

How is a confidence interval calculated?

A confidence interval is calculated using a formula that takes into account the sample size, sample mean, and standard deviation. The most common formula is: sample mean ± (critical value x standard deviation / √sample size), where the critical value is based on the desired level of confidence and the sample size.

What is the significance of the level of confidence in a confidence interval?

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

How does sample size affect the width of a confidence interval?

Generally, as the sample size increases, the width of the confidence interval decreases. This is because a larger sample size provides more information and reduces the uncertainty in the estimate. However, other factors such as the standard deviation and level of confidence also play a role in determining the width of the interval.

What is the relationship between confidence interval and margin of error?

The margin of error is the maximum amount that the sample statistic may differ from the true population parameter. It is directly related to the width of the confidence interval, as a narrower interval means a smaller margin of error. In other words, a more precise estimate will have a smaller margin of error and a narrower confidence interval.

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