Often when comparing two populations there is a need to see if there is a significant difference between the means of the populations. e.g. Comparing the average weights of fruit from two different trees.
In the same way that the Central Limit Theorem allowed us to calculate the mean and standard deviation for the sample means and sample proportions, we can do the same for the difference between two means.
Thus confidence intervals for the difference between two means can be calculated to a desired degree of confidence, provided the two samples are independent.
Degree of Confidence
The degree of confidence is given as a percentage probability changed into a zscore, using the standard normal distribution tables. Check out where the zscores 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 Difference between Two Means
The confidence interval, based on the Central Limit Theorem, for the difference between two means μ_{1} and μ_{2 }is given by:
where n_{1} and n_{2} are the sizes of the two samples and is the standard deviation of the sampling distribution of the difference between two sample means.
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 deviations (σ_{1 }and σ_{ 2 }) are not known the sample standard deviations can be used as an estimate.
Example
A sample of 60 students from schools in the North Island has a mean weight of 55 kg with a standard deviation of 5 kg. A sample of 50 students from schools in the South Island has a mean of 53 kg and a standard deviation of 7 kg. Find a 99% confidence interval for the difference between the means of the population of students in the two islands.
Let n_{1} = 60 and n_{2} = 50
= 55 and = 53 Use the sample standard deviations s_{1} = 5 and s_{2} = 7 z = 2.576 for a 99% level of confidence.
Using =
The 99% confidence interval is (1.04, 5.04)
This can be written as 1.04 < μ_{1} and μ_{2} < 5.04.
As this interval includes 0, this means that the two populations could have the same mean.
