When a sample is taken from a population, we can use sample statistics, such as 
and s as point estimates for the population parameters, μ and σ.
A one-off sample such as this could be a very inaccurate estimator however and it is best to find a range or interval of values and say with some level of probability that the population parameters will lie within this range. This range of values is called a confidence interval for the mean.
Degree of Confidence
The degree of confidence is given as a percentage probability changed into a z-score, using the standard normal distribution tables. Check out where the z-scores are found in the tables for the following degrees of confidence.
Any degree of confidence can be found in this manner. e.g. For an 80% level of confidence look up 0.40 in the tables.
Confidence Interval for the Mean
The confidence interval, based on the Central Limit Theorem, for the mean μ is given by:
 ± z .  |
Remember that is called the standard error of the mean.
The value of z will depend upon the degree of confidence required. The z values for 90, 95 and 99% are shown above.
If the population standard deviation (σ) is not known the sample standard deviation (s) can be used as an estimate.
With a 95% confidence interval, this means that if we know the population standard deviation, σ, we could take a random sample of size n, find its mean
Example
A dairy factory produces blocks of cheese for export whose weights are known to be normally distributed with a standard deviation of 1.4 kg. A random sample of 25 blocks of cheese has a mean weight of 22.3 kg. Find 99% confidence intervals for the population mean.
The 99% confidence interval is:
± 2.576 .
= 22.3 ± 2.576 . 
= 22.3 ± 0.721
= (21.579 , 23.021)
The 99% confidence interval for μ is (21.579 kg, 23.021 kg)
This is sometimes written 21.579 < μ < 23.021
Meaning of Confidence Intervals
The answer in the example above means that 99% of such confidence intervals will contain the population mean.
