In the previous post we surveyed some of the areas where statistics are essential in the disciplines, ending that there was insufficient space to cover statistics in governance. While governance covers a broad range of activities, for now let us just explore government and, more specifically, a democracy. In democracy, citizens vote on who should govern with the goal of public good. In addition to making rules or laws, the elected officials determine what is needed for society individually and collectively, the priorities for action and how the costs are going to be paid. It is similar to a family without an election. Choices are made, bills are paid, and long term obligations are incurred.
Previously we looked at insurance companies and actuarial science. While a government is not a for profit company, the same data is essential to long term planning. The average age (is it median, mode, or mean?) of the population affects the distribution of workers essential to sustaining revenues for those who cannot work or earn. Similarly, education and training credential distribution is part of the demographics (statistical studies of the population) that affects the capacity of the population to pay its bills and long term obligations. If the statistics are wrong, crisis and/or failure might result.
Since the goal is public good, clearly biostatistics, including medical statistics, is essential. We need to know how many people are well, sick, threatened, and contagious. We also need to know the projected numbers of people that will need treatment and who is responsible for the cost. And, perhaps most important, when is the population threatened. We need the statistics of epidemiology to determine the interventions to be made in the interest of public health and preventive medicine.
Above, we briefly mentioned education and training. Clearly, a democracy, with its obligation for the public good, needs to measure knowledge, skills, abilities, and personality traits. This is the domain of psychometrics. As an oversimplified example, consider a salesperson in a store, a high school English teacher, and a quantum physicist. The sales person probably cannot teach English or do quantum science. Probably all three are mutually exclusive, especially the quantum physicist teaching Shakespeare. Notice I said probably. Is there a population distribution of physicists versus English teaching ability?
While this example may cause a chuckle, recall that the U. S. Military Services used “IQ” testing during World War I to select officers. It was driven by an immediate need. A similar need today might be for cyber security analysis to ensure the national defense. How many cyber experts do we need and how fast do we need them? Are the statistics correct and, by the way, what is a cyber expert? In a democracy we do not dictate what a person must do in the sense of a specific work domain, usually, generally, most of the time. Yet in the past we have drafted people into military service because of external threats. How are they sorted? It’s in the statistics.
Shifting from people, a democracy must worry about the environment, The Los Alamos World Futures Institute focuses on humanity and the environment that sustains it – Earth. Enter the field of environment science and the application of environment statistics. It includes weather, climate, air and water quality as well as plant and animal populations. A long time ago (OK, short), Thomas Malthus wrote An Essay on the Principle of Population (1798). It dealt with sustaining a population of 800 million people and being able to provide food, water, shelter and other elements of Maslow’s Hierarchy of Needs. Today the world population is around 6.7 billion. Obviously, humanity has overcome the obstacles envisioned by Malthus and the statics have changed and continue to change. Of course, one might ask how do decisions from geostatistics (dealing with petroleum, hydrology, hydrogeology, etc.) affect or influence environment science statistics and decisions?
Finally, in this simplified survey, how does government pay for everything? Like the family unit, it comes from earned income and borrowing. Earned income is government-speak for taxes – you can argue about the earned part, but it goes beyond the simple view of compensation for labor performed. To make choices about your money, you first (should) estimate your income and capacity to meet your obligations. As an individual, the data flow and analysis is pretty straight forward, yet we make mistakes sometimes because of emotional influence. In government spending we can use statistical finance (an area of econophysics) and lots of statistical models to statistically project anticipated income and “balance the budget.” But do we? Do we fully understand the statistical error margins and do we sometimes make collective emotional decisions? How do we understand what people really want?
Till next time….