A Summary of the Most Relevant Types of Statistical Analysis.

There are basically two different forms of statistics which are pretty much related to but still different from each other. Primarily, we have descriptive statistics and the second is inferential statistics.

Descriptive statistics corresponds to simply the process of defining characteristics of a statistical measurement from a population. Descriptive statistics consist of the mechanisms and methods used to organize and summarize raw data. There are several ways statisticians acomplish this. Charts and graphs play an important role, plus some standard measurements such as averages, percentiles, and measures of variation, such as the standard deviaton.

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 observing the raw data and summarizing, categorizing to come up with statements of fact regarding the season. There are many examples that would make this apparent. Consider this, for instance. In 1948 more than 600 games were played in the League. Determining who had the best batting average in that year, you would need to take the official score sheets for each game, make a 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. The statistics indicate that the best player in 1948 was Ted Williams. On the other hand finding out who were the 25 top players at a given year demands a more laborious calculation, clearly.

The advent of the new generation of computers has created a different scenario, though. Now, statisticians possess tools they never imagined before. 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.

Inferential statistics consists of 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 one 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 (or even less) carefully chosen Americans are asked which way they will be voting. From the answers given to this question, statisticians are able to predict, or infer who the general population will vote for with a reasonable confidence level. Obviously, the two keys to inferential statistics are choosing the righ sample of members of the general population will be polled and which questions are asked. In a case such as the above, with two candidates, and the polled population, or sample population is asked: Will you give your vote to Candidate X in the next election the only alternatives for the answer will be either yes, no, or undecided. Based on the results you should be able to determine that 51% of the sample group (for instance) will Give their vote to Candidate X.

Applying techniques of inferential statistics, we can {predict in most of the cases that Candidate X will win the election. Nevertheless, we have to be careful because the 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 has proven many times, inferential mistakes happen and Truman won more than 49% of the votes and ultimately, and the end result is that he won the election. This incident changed the way samples were obtained, and much more scientific procedures were devised to assure that more precise predictions are obtained.