The field of inferential statistics enables you to make educated guesses about the characteristics of large groups. The logic of sampling gives you a way to test conclusions about such groups using only a small portion of its members.
What's inferential statistics all about?
A population is a group of phenomena that have something in common. The term often refers to a group of people — such as all registered voters in Crawford County, or all Americans who played golf at least once in the past year.
But populations can refer to things as well as people — such as all widgets produced last Tuesday by the Acme Widget Company, or all daily maximum temperatures in August for major U.S. cities.
Often, researchers want to know things about populations but don't have data for every person or thing in the population. If a company's customer service division wanted to learn whether its customers where satisfied, it wouldn't be practical (or perhaps even possible) to contact every individual who purchased one of their products. Instead, they might select a sample of the population.
A sample is a smaller group of members of a population selected to represent the population. In order to use statistics to learn things about the population, the sample must be random. A random sample is one in which every member of a population has an equal chance to be selected.
A parameter is a characteristic of a population. A statistic is a characteristic of a sample. Inferential statistics enables you to make an educated guess about a population parameter based on a statistic computed from a sample randomly drawn from that population.