Title: Estimating the value of a characteristic
Course: UGQuants (SSPS) > Section: Probability

Estimating the value of a characteristic

So far we have calculated confidence intervals by hand. However, once you understand the logic of the calculations, you can safely leave the number crunching to the computer.

Suppose we are interested in the gross domestic product of countries with a female life expectancy of up to 65 years compared to countries with a female life expectancy over 65 years (as good researcher, you will have a reason for forming these groups).. Using the SPSS command descriptives (confusingly, for two variables, these are found under the label ‘explore’) we can ask for various statistics of this variable, including the mean and the confidence interval for the mean.

SPSS will give us the following output (the precise format will depend on what statistics you ask for in the options dialog box):

Table 1 Confidence intervals for gross domestic product by female life expectancy.

The gdp of countries with high female life expectancy and of countries with lower female life expectancy are both population parameters. They are characteristics of the population we wish to estimate. Since we lack information on 26 countries, we could say that while the WDI dataset strives to represent the world population, in this case it only holds information of a (large) sample.

The SPSS output tells us the sample mean, for both men and women, the standard error of this mean (which SPSS calculates from the sample standard deviation for this characteristic and the size of the sample - just as you did manually in the previous module), and the upper and lower limits for the Confidence Interval we asked for. If you look at the figures in the table, you will notice that they are presented with a lot of decimals: when reporting these figures, it is better to round these, because you cannot actually claim to have such detailed knowledge – inferential statistics only ever present estimates.