First, we start with Descriptive statistics. Descriptive statistics corresponds to simply the act of defining characteristics of a statistical measurement. In genereal terms, descriptive statistics includes the use of a observational study of a population, achieved by summarizing data obtained from a random sample. In order to categorize the raw data that is collected, the majority of statisticians use graphs, charts, tables and standard measurements such as averages, percentiles, and measures of variation.

Also, descriptive statistics are frequently used in the course of a baseball season. In fact, baseball statisticians spend a lot of effort and resources examining the data they get from the games and summarizing, categorizing to discover regularities to enlighten the audience. Think of this, for example. In 1948 more than 600 games were played in the American League. To determine who had the best batting average in that season, you would need to take the official score sheets for each of the games, make a list each batter, compute 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. On the other hand knowing who were the 25 best players at a given season forces you to go for a more laborious calculation, clearly.

The use of computer statistical programs and the ability to incorporate a lot of statistical functions on spreadsheet programs such as Excel implies that more and more complicated and detailed information can be collected, formatted and presented with only a few clicks of the mouse. The imaginary games and sports events developed through the use of a computer software program is essentially the collection of massive amounts of data and finding correlations 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 group based upon data obtained from a sample of the group. Among the many uses of inferential statistics, political predictions ar a very good example. In order to be able to try to predict who the winner of a presidential election is more likelly to be, in most of the cases a sample of a few thousand carefully chosen Americans are asked for their vote intention. With this answers statisticians are able to predict, or infer who the general population will vote for with a surprinsingly high level of confidence. Obviously, the two keys to inferential statistics are choosing which members of the general population will be polled and which questions are 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, we can {predict with a certain degree of confidence that Candidate X will win the election. However, in some instances, the sampling procedure could have given rise to invalid inferences. A classic example is the 1948 Presidential election. The preliminary results posted by Gallup made the wrong impression and President Harry Truman believed he would get approximately 45% of the votes which would imply losing to 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 obtained.