The objective of this article is to give a general idea of the difference between the types of statistical analysis. The two main areas in the statistical science are Descriptive and Inferential statistics. These two branches are tighly associated, but are able to absolutely differentiate between them.

First, we start with Descriptive statistics. Descriptive statistics corresponds to essentially the mechanism of measuring characteristics from a population. Descriptive statistics is based upon procedures and methods used to organize and summarize raw data. In order to put the data obtained in categories, the majority of statisticians rely on graphs, charts, tables and standard measurements such as averages, percentiles, and measures of variation.

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 obtain from the games and summarizing, categorizing to discover regularities to enlighten the audience. For example 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 scores for each of the games, make a list each batter, determine the results of each time at bat, add the total number of hits and the total number of times at bat in order to calculate with a batting average. In 1948 the American League player with the highest batting average was Ted Williams. On the other hand knowing who were the 25 top players at a given year demands a quite more complex, no doubt about it.

The introduction of the new generation of computers has created a different scenario, though. Now, statisticians possess tools they never conceived before. Applications now bring statistical functions that make this calculations a breeze. The imaginary games and sports events developed through the use of a computer software program is fundamentally the collection of big amounts of data and correlating it in such a way as to be able to make comparison among similar activities.

Inferential statistics is the process of 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. Among the many uses of inferential statistics, political predictions ar one good example. In order to be able to attempt to predict who the winner of a presidential election is likely to be, in most of the cases a sample of a few thousand carefully chosen 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 polled and what questions are asked. Imagine a situation 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 answer will be either yes, no, or undecided. From the descriptive statistics you can determine that 51% of the sample group will vote for Candidate X.

Turning to inferential statistics, we can {predict with a certain degree of confidence that Candidate X will be the winner in the election. However, in some instances, the sampling procedure may have given rise to incorrect inferences. A classic example is the 1948 Presidential election. Based on a poll obtained by the Gallup Organization, President Harry Truman believed he would get approximately 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 of course, won the election. This incident changed the way samples were obtained, and much more rigorous procedures were developed to assure that more precise predictions are cast.

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