Statistical Analysis: A Summary Of The Most Important Types. Article

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Statistical Analysis: A Summary of the most Important Types.

The use of Descriptive and Inferential Statistics. I talk briefly about the fundamental features of the most relevant types of statistical analysis

Author: Robert Duval

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We know of two different branches of statistics which are pretty much related to but still different from each other. Primarily, we have descriptive statistics and then is inferential statistics.

First, we start with Descriptive statistics. Descriptive statistics corresponds to simply the act of defining characteristics of a statistical measurement. Descriptive statistics consist of the mechanisms and methods used to organize and summarize raw data. In order to categorize the data from a random sample that is collected, the majority of statisticians use tables, charts, graphs and standard measurements such as averages, percentiles, and measures of variation.

One of the most common uses of descriptive statistics is in sports (all kind of sports). In fact, baseball statisticians spend a lot of effort and resources 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 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 season, you would need a lot of effort. You would need to take the official scores for each of the games, make a list each batter, determine the results of each time the player is at bat, add the total number of hits and the total number of times the player is at bat in order to come up with a batting average. In 1948 the American League player with the top batting average was Ted Williams. On the other hand knowing who were the 25 top players at a given season demands a quite more complex, no doubt about it.

The use of computer statistical programs and the ability to incorporate many statistical functions on spreadsheet programs such as Excel means that the complexity of the data we can collect becomes more detailed, and it can be formatted and presented with only a a few keystrokes. All this have empowered the sport statisticians to a further degree and they are able to manage massive amounts of information and explore the data in a way more systematic fashion.

On the other hand, 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. 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 more likely to be, in most of the cases a sample of a few thousand (or even less) carefully chosen sample of Americans are asked for their vote intention. From the answers given to this question, statisticians are able to predict, or infer who the general population will vote for with a surprisingly high level of confidence. Clearly, the two keys to 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 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.

Applying methods of inferential statistics, you can {predict with a certain degree of confidence that Candidate X will win the election. However, in some cases, the sampling procedure could have given rise to invalid inferences. A classic example is the 1948 Presidential election. The preliminary results posted by Gallup made the wrong impression and 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 proves, Truman won more than 49% of the votes and ultimately, won the election. This caused a change in some of the sampling techniques and the Gallup Organization has accurately predicted the Presidential election winner since.

About Author

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

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Statistical Analysis: A Summary of the most Important Types.