Confidence intervals and range of possible values

In summary, the conversation discusses the concept of confidence intervals and how they are calculated for different parameters. It is mentioned that a confidence interval does not describe a range of possible values, but rather a probability that the true value of the parameter falls within that interval. The conversation also touches on the uncertainty of using sample statistics to estimate the population mean, and suggests using the average of the sample values along with a confidence interval to do so. It is also mentioned that for a Gaussian distribution, the confidence interval is the mean plus or minus the t statistic times the standard error.
  • #1
striphe
125
1
When i did a business statistics course some time ago, I was able to calculate confidence intervals, but i didn't understand ‘why’ they were calculated in the way they were.

I considered that the size of a confidence interval is based on the number of observations and ‘the range of possible values’ that the those observations may result in. When calculating confidence intervals for the mean of a population, you use standard deviation rather than the range of possible vales.

The reason i considered the range of possible values rather than the variance or standard deviation, is because they are a statistic of their own and could have a confidence interval applied to them. Something suggests to me that the confidence interval of variance can not be based the number of observations alone and that if the confidence interval of variance is dependent on another factor, then the confidence interval of the variance or standard deviation would affect the confidence interval of the mean.

So where have I gone wrong here?
 
Physics news on Phys.org
  • #2
A confidence interval does not describe a "range of possible values." (This sounds more like a prediction interval.) A confidence interval at the x% level for a certain parameter will contain the true value of that parameter x% of the time. For example, a confidence interval for the mean of a sample can be calculated from the sample standard deviation and the number of observations (it's approximately [itex]1.96\mathrm{SD}/\sqrt{N}[/itex] for a Gaussian distribution, a 95% CI, and large [itex]N[/itex]), but it's not the same as the sample standard deviation. Also, there is a confidence interval for the sample standard deviation itself (and this confidence interval may or may not contain the true population standard deviation), but that's a side issue that doesn't enter into the question of how to estimate the true population mean. Does this answer your question?
 
  • #3
when we did our calculations, i think we were given the population SD.

The smaller your population SD the smallar the confidence interval (not percentage size, but value size). I considered the fact that there is uncertainty with regards to the population SD that one should use the maximum possible SD for a population. This maximum would result if all the observations were evenly the highest possible and lowest possible observation. As it is assumed that the population is unlimited any sample results will be outweighed entirely by the possible highest and lowest observations.

So on second thought my question is how does one estimate the population mean, with only sample statistics?
 
  • #4
striphe said:
So on second thought my question is how does one estimate the population mean, with only sample statistics?

With the average of the sample values.
 
  • #5
And give it a confidence interval
 
  • #6
Is the data Gaussian? If so, the confidence interval is the mean plus or minus the t statistic times the standard error. (For large N, the 95% t statistic is 1.96, as I wrote above.) More http://en.wikipedia.org/wiki/Confidence_interval" .
 
Last edited by a moderator:

FAQ: Confidence intervals and range of possible values

What is a confidence interval?

A confidence interval is a range of values within which we can be confident that the true value of a population parameter lies. It is calculated from a sample and is used to estimate the true value of the parameter in the entire population with a certain level of confidence.

How is the confidence interval determined?

The confidence interval is determined by taking the point estimate (usually the sample mean) and adding or subtracting a margin of error, which is calculated using the sample size, standard deviation of the sample, and the desired level of confidence. The resulting range of values is the confidence interval.

What does the confidence level represent?

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

Why is it important to report the confidence interval?

Reporting the confidence interval along with the point estimate provides a more complete understanding of the data. It gives an idea of the precision of the estimate and allows for comparison between different samples. It also helps to communicate the uncertainty associated with the estimate.

How does the sample size affect the confidence interval?

The sample size has a direct impact on the confidence interval. As the sample size increases, the confidence interval becomes narrower, meaning there is less uncertainty in our estimate. With a larger sample size, the margin of error decreases, resulting in a more precise estimate of the population parameter.

Similar threads

Replies
3
Views
994
Replies
22
Views
3K
Replies
4
Views
1K
Replies
1
Views
904
Replies
1
Views
950
Replies
6
Views
3K
Replies
18
Views
3K
Back
Top