The Different Types of the Statistical Analyses.

The objective of this paper is to put some light on the difference between the difference forms of statistical analysis. The two main basic branches in science known as statistics are Descriptive and Inferential statistics. These areas are tighly associated, but yet we can clearly see the difference between their roles and objectives.

Descriptive statistics is essentially the mechanism of measuring characteristics from a population. Descriptive statistics consist of the procedures and methods employed to organize and summarize raw data. There are several alternatives for statisticians to acomplish this objective. Charts and graphs play an important role, plus some standard measurements such as averages, percentiles, and measures of variation, such as the standard deviaton.

Also, descriptive statistics are frequently employed during a baseball season. Baseball statisticians spend a lot of effort and resources looking at the raw data and summarizing, categorizing to discover regularities to enlighten the audience. For example in 1948 more than 600 games were played in the American League. Determining who had the best batting average in that year, you would need a lot of effort. You would need to take the official score sheets for each of the games, list each batter, compute the results of each time 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 year forces you to go for a quite more complex, clearly.

The advent of the new generation of personal computers has created a different scenario, though. Now, statisticians possess tools they never imagined before. Applications now include statistical functions that make everything easier. The imaginary games and sports events developed through the use of a computer software program is essentially the collection of big amounts of data, and correlating it in such a way as to be able to compare like activities.

Inferential statistics is based upon choosing and measuring the trustworthiness 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 attempt to predict who the winner of a presidential election is more likelly to be, typically a sample of a few thousand carefully chosen sample of 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. Clearly, the two keys to inferential statistics are choosing the righ sample of members of the general population will be polled and what questions are asked. In a case such as the above, where there is a choice of 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. From the descriptive statistics you should be able to determine that 51% of the sample group (for instance) will Give their vote to 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. Nevertheless, in some cases, the sampling procedure could have given rise to incorrect inferences. Let's not forget of the classic case of the 1948 Presidential election. The preliminary results posted by Gallup made the wrong prediction and President Harry Truman believed he would only gain about 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 rigorous procedures were developed to assure that more accurate predictions are delivered.

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