Statistical Analysis: A Summary Of The Most Important Types

By Author: Robert Duval
Total Articles: 25
Comment this article

The two main basic branches in statistics are Descriptive and Inferential statistics. These areas are tighly associated, but are able to clearly establish a differentiation between their roles and paradigms.

First, we begin with Descriptive statistics. Descriptive statistics is simply the process of measuring characteristics from a population. Descriptive statistics consist of the procedures and methods employed to organize and summarize raw data. In order to categorize the raw data that is gathered, the majority of statisticians rely on graphs, charts, tables and standard measurements such as averages, percentiles, and the corresponding measures of variation.

Descriptive statistics are frequently employed during a baseball season. In fact, baseball statisticians spend a great deal of time and effort looking at 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. For example in 1948 there were over 600 games 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 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 come up with a batting average. The statistics showed that the winner in 1948 was Ted Williams. On the other hand finding out who were the 25 best players at a given year demands a more laborious calculation, clearly.

The introduction of the new generation of computers has changed everything, though. Now, statisticians possess tools they never imagined before. Applications now include statistical functions that make this calculations a breeze. The imaginary games and sports events developed by using computer applications is essentially the collection of massive amounts of data, and finding correlations in such a way as to be able to compare like activities.

Inferential statistics consists of choosing and measuring the validity of conclusions about a group based upon data obtained from a sample of the group. Among the many possible uses of inferential statistics, political predictions ar one good example. 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 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 surprinsingly high level of confidence. Obviously, the two keys to inferential statistics are choosing which members of the general population will be polled and which questions will be 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. Based on the results 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, you can {predict in most of the cases that Candidate X will be the winner in the election. Nevertheless, we have to be cautious because the 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 get approximately 45% of the votes which would imply losing to 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, won the election. This incident changed the way samples were collected, and much more rigorous procedures were created to assure that more precise predictions are delivered.