The Different Forms of Statistical Analyses.
Descriptive statistics corresponds to essentially the mechanism of measuring characteristics from a population. Descriptive statistics is based upon mechanisms and methods employed to organize and summarize raw data. In order to categorize the data from a random sample that is collected, most statisticians use graphs, charts, tables and standard measurements such as averages, percentiles, and the corresponding measures of variation.
Also, descriptive statistics are frequently employed in the course of 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. There are many examples that would make this clear. 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, make a 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 highest batting average was Ted Williams. But, if you really wanted to calculate who the top 25 players for the current or past years were, the statistical computations would be increasingly complicated.
The use of computer statistical software and the capability to use a lot of statistical functions on spreadsheet programs such as Excel implies 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. 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 finding correlations in such a way as to be able to make comparison among similar 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 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 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 reasonable confidence level. Clearly, the fundamental elements in inferential statistics are choosing which members of the general population will be chosen and which 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 (for instance) will vote for Candidate X.
Turning to inferential statistics, you can {predict with a certain degree of confidence that Candidate X will be the winner in the election. Nevertheless, we have to be careful because the the sampling techniques could have created incorrect inferences. A classic example is the 1948 Presidential election. Based on a poll taken by the Gallup Organization, President Harry Truman believed he would get approximately 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 caused a change in some of the sampling techniques and the Gallup Organization has correctly predicted the Presidential election winner ever since.
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