From central limit theorem we know that,
is a standard normal variate. From the discussion of introduction to interval estimation we know that P (–1.96 < Z< 1.96) = 0.95 has the least possible range. With this inequality we can construct a 95% confidence interval estimate of the population mean, if we replace Z by
In general if the margin of error in our interval estimate is (this is also referred to as the level of significance) then the level of confidence would be or , for example, if or 5% then or 95% would be the level of confidence. To have the shortest of the interval the margin of error is distributed equally on the two sides of the curve as shown in the figure:
Thus, for , the curve has the margin of error = (0.025) on the left tail and the same area on the right tail. The value Z which gives an area on the left tail is denoted by and that which gives an area equal to on the right tail is denoted by. The area enclosed between and would therefore be. In our example, for , and have values –1.96 and 1.96 respectively and .
From the above discussion we can write the following probability statement. The 100% confidence interval estimate of mean can be obtained by replacing Z with, , thus
The 100% lower and upper confidence limits are, therefore, . As a specific example let, so that or 90%, the probability statement may thus be written as [Since , ] Consulting the normal table, hence, we can write P [–1.64
The lower confidence limit is
Also the upper confidence limit is
Hence, the 99% confidence interval estimate for the mean score will be, , i.e., the mean score is between 55 and 65.