Statistics

Two fundamental concepts used in statistical analysis are population and sample. The term population refers to a complete set of individuals, objects, or events that belong to some category. For example, all of the players who are employed by Major League Baseball teams make up the population of professional major league baseball players. The term sample refers to some subset of a population that is representative of the total population. For example, one might go down the complete

list of all major league baseball players and select every tenth name. That subset of every tenth name would then make up a sample of all professional major league baseball players.

Another concept of importance in statistics is the distinction between discrete and continuous data. Discrete variables are numbers that can have only certain specific numerical value that can be clearly separated from each other. For example, the number of professional major league baseball players is a discrete variable. There may be 400 or 410 or 475 or 615 professional baseball players, but never 400.5, 410.75, or 615.895.

Continuous variables may take any value whatsoever. The readings on a thermometer are an example of a continuous variable. The temperature can range from 10°C to 10.1°C to 10.2°C to 10.3°C (about 50°F) and so on upward or downward. Also, if a thermometer accurate enough is available, even finer divisions, such as 10.11°C, 10.12°C, and 10.13°C, can be made. Methods for dealing with discrete and continuous variables are somewhat different from each other in statistics.

In some cases, it is useful to treat continuous variable as discrete variables, and vice versa. For example, it might be helpful in some kind of statistical analysis to assume that temperatures can assume only discrete values, such as 5°C, 10°C, 15°C (41°F, 50°F, 59°F) and so on. It is important in making use of that statistical analysis, then, to recognize that this kind of assumption has been made.