Statistics is a growing field, particularly in those fields dealing with human behavior.
Statistics is one of the few fields for which the U.S. Department of Labor forecasts a faster rate of growth in jobs than for computer science. The widespread use of statistics is very much a twentieth-century phenomenon. An increasingly important aspect of modern statistics is the design of experiments and surveys -- as well as analyzing the resulting data -- in areas of application ranging, literally, from archeology to zoology.
Statisticians have also been responsible for the development of quasi-experiments in which, to use medical terminology, the researcher has no control over which patient receives which treatment. Examples include the numerous studies of the effects of smoking on health. Let's look at an example.
Lung cancer rates are far higher for smokers than for non-smokers, but this does not "prove" that smoking causes lung cancer. People decide whether or not to smoke and it can be argued that some factor X, perhaps unknown to us, is responsible for producing both an urge to smoke and lung cancer.
An "ideal" but morally repugnant experiment might involve perhaps 1000 pairs of identical twins. One of each pair would be assigned at random to smoke from the age of sixteen and the other instructed not to smoke at all. Assigning the twins randomly in this way would ensure that the factor X -- if it existed -- would be distributed close to evenly between the two groups. Any differences in lung cancer rates between the smoking and non-smoking groups cannot be due to factor X but can be attributed to cigarettes.
Lacking such an "ideal," statisticians have developed studies in which subjects are "matched" in as large a number of factors -- age, sex, race, disposition, etc. -- as possible so as to control for the effects of these factors.
Statisticians use surveys -- for example, opinion polls and the Government's Health Interview Survey -- to predict the patterns of behavior of large groups based on relatively small samples. They ask questions such as: How can we be sure that what we predict from our small sample is true of the population being sampled? Questions like this are answered using probability theory. This is a branch of mathematics which is important in its own right, but which also provides the theoretical underpinning for statistics.
Related to both probability and statistics is queuing theory, which predicts what happens as people or things get in line. Typically you will know the average rate at which the things enter the system, but entries will be random otherwise. Queuing theory can be used to predict how traffic bunches up at toll booths, how lines grow and shrink at restaurants or in hospital emergency rooms, and how long your job takes to be printed out from a computer printer. If your local bank or post office is now using one long line that "feeds" to several tellers or windows, that is because queuing theory tells us that system will minimize the expected wait. So you can see that this part of statistics has many applications.
Another area in which there is a need to predict events is insurance. The people responsible for computing insurance rates are specialist statisticians called actuaries. To become an actuary you take a series of ten written exams that are given regularly by the Society of Actuaries. The first exam is on calculus and the second is on probability and statistics, which you can take as an undergraduate math major. The insurance companies offer courses to prepare you for the later exams and even offer you time off to study for them. Typically you can expect good raises and promotions as you pass these exams. (And because they are standardized tests, you know that you will not be discriminated against.) Actuaries have a lot of responsibility and, not surprisingly, are very well paid.
This completes our brief summary of statistics. We have tried to provide a flavor of the kinds of problems statisticians solve, working both singly and in collaboration with people in other fields. Part of the fun of being a statistical data analyst is the rich availability of opportunities to work closely with professionals in a wide variety of specialties: medicine, economics, business, science, and other fields.