Statistical Analysis: A Summary of the most Important Types.

We know of two different forms in 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 essentially the process of measuring characteristics from a population. Roughly speaking, descriptive statistics includes the use of a observational study of a population, which is achieved by summarizing data obtained from a random sample. There are several alternatives for statisticians to acomplish this objective. They rely on tables, charts, graphs and standard measurements such as averages, percentiles, and measures of variation.

Also, descriptive statistics are often 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 come up with statements of fact regarding the season. 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 season, you would need a lot of effort. You would need to take the official score sheets for each game, 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 finding out who were the 25 top players at a given season forces you to go for a quite more complex, no doubt about it.

The wonders of the new generation of computers has created a different scenario, though. Now, statisticians possess tools that a few years ago would have been very difficult to anticipate. Software packages now include statistical functions that make everything easier. All this have given more power to the sport statisticians to a further degree and they are capable to manage massive amounts of information and explore the data in a more systematic fashion.

Now, 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. Political polling is a great example of inferential statistics. In order to determine who the winner of a presidential election is more likelly to be, typically 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 reasonable confidence level. Obviously, the fundamental elements in inferential statistics are choosing which members of the general population will be chosen and what questions are asked. Imagine a situation 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 can determine that 51% of the sample group will Give their vote to Candidate X.

Turning to inferential statistics, you can {predict with a certain degree of confidence that Candidate X will win the election. However, in some instances, the sampling techniques could have given rise to incorrect inferences. A classic example is 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 and would lose to Republican challenger Thomas Dewey. As a matter of fact, as history proves, 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 rigorous methods were devised to assure that more accurate predictions are delivered.

Robert collaborates with StatisticsBrain, a tutoring resource that offers help with statistics homework.

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