A Summary of the Most Notoriuos Types of Statistical Analysis

There are fundamentally two different types of statistics which are related to but yet there is a clear distinction between them. The first is descriptive statistics and the second is inferential statistics.

First, we begin with Descriptive statistics. Descriptive statistics is basically the process of defining characteristics of a statistical measurement from a population. Descriptive statistics consist of the mechanisms and methods employed 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.

Also, descriptive statistics are commonly employed in the course of a baseball season. In fact, baseball statisticians spend a great deal of time and effort observing the raw data and summarizing, categorizing to discover regularities to enlighten the audience. There are many examples that would make this clear. Consider 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 year, you would need a lot of effort. You would need to take the official scores for each of the games, 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 top batting average was Ted Williams. But, if your goal is to calculate who the top 25 players for the season were, the statistical computations would become increasingly complicated.

The use of computer statistical programs and the capability to use a lot of statistical functions on spreadsheet programs such as Excel means 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 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 compare like activities.

Now, 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. Political polling is a great 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 carefully chosen sample of 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 the righ sample of members of the general population will be chosen and which 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: Are you planning to vote for X in the upcoming 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 vote for Candidate X.

Applying methods of inferential statistics, you can {predict with a certain degree of confidence that Candidate X will be the winner in the election. However, we have to be careful because the the sampling procedure could have created invalid inferences. Let's recall the classic case of the 1948 Presidential election. Based on a poll obtained 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. As a matter of fact, as history has proven many times, inferential mistakes happen and Truman won more than 49% of the votes and of course, and the end result is that he won the election. This incident changed the way samples were obtained, and much more rigorous procedures were devised to assure that more precise predictions are obtained.

Robert runs StatisticsBrain, a tutoring resource that offers statistics homework help online.

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