There are fundamentally two different forms in statistics which are related to but yet there is a clear distinction between them. Primarily, we have descriptive statistics and then is inferential statistics.

First, we start with Descriptive statistics. Descriptive statistics is essentially the process of measuring characteristics from a population. Roughly speaking, descriptive statistics includes the use of a observational study of a population, which is achieved by summarizing and organizing data from a random sample. In order to categorize the raw data that is collected, the majority of statisticians rely on graphs, charts, tables and standard statistical measurements such as averages, percentiles, and measures of variation.

One of the most common uses of descriptive statistics is in sports (all kind of sports). Baseball statisticians spend a great deal of time and effort examining the data they get from the games and summarizing, categorizing to discover regularities to enlighten the audience. There are many examples that would make this apparent. Consider this, for instance. In 1948 more than 600 games were played in the American League. Determining who had the best batting average in that season, you would need to take the official scores for each game, list each batter, determine the results of each time the player is at bat, and proceed to count the total number of hits and the times at bat. In 1948 the American League player with the highest batting average was Ted Williams. But, if you really wanted to calculate who the top 25 players for the season were, the statistical computations would be increasingly complicated.

The wonders of the new generation of computers has created a different scenario, though. Now, statisticians possess tools that a few years ago would have been near to impossible to imagine. Applications now include statistical functions that make this calculations a breeze. The imaginary games and sports events developed by using computer applications is essentially the collection of massive amounts of data, and correlating it in such a way as to be able to make comparison among similar activities.

On the other hand, inferential statistics is based upon choosing and measuring the validity of conclusions about a population parameter based on information from a reduced portion of that population, which is a random sample. Political polling is an excellent example of the way inferential statistics are used. In order to determine who the winner of a presidential election is likely to be, typically a sample of a few thousand (or even less) carefully chosen sample of Americans are asked which way they will be voting. With this answers statisticians are able to predict, or infer who the general population will vote for with a surprinsingly high level of confidence. Clearly, the fundamental elements in inferential statistics are choosing which members of the general population will be chosen and which questions will be asked. Imagine a situation with two candidates, and the polled population, or sample population is asked: Will you vote for Candidate X in the next election? the answer will be either yes, no, or undecided. Based on the results you can determine that 51% of the sample group (for instance) will Give their vote to Candidate X.

Applying techniques of inferential statistics, you can {infer in most of the cases that Candidate X will win the election. Nevertheless, in some cases, the sampling procedure could have created incorrect inferences. Let’s recall the classic case of the 1948 Presidential election. Based on a poll taken by the Gallup Organization, President Harry Truman believed he would only gain about 45% of the votes and would lose to Republican challenger Thomas Dewey. In fact, as history proves, Truman won more than 49% of the votes and ultimately, won the election. This incident changed the way samples were obtained, and much more scientific methods were developed to assure that more accurate predictions are cast.