Descriptive statistics corresponds to essentially the act of defining characteristics of a statistical measurement. 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, the majority of statisticians use graphs, charts, tables and standard measurements such as averages, percentiles, and measures of variation.
Descriptive statistics are often employed during a baseball season. Baseball statisticians spend a great deal of time and effort examining the data they get from the games and summarizing, categorizing to discover regularities to enlighten the audience. There are many examples that would make this apparent. For example in 1948 there were over 600 games 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 game, make a list each batter, compute 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 top batting average was Ted Williams. But, if your objective is to know who the top 25 players for the year were, the statistical calculations would become increasingly complicated.
The use of computer statistical programs and the capability to incorporate a lot of statistical functions on spreadsheet programs such as Excel implies that more and more complicated and detailed information can be collected, formatted and presented with only a a couple of keystrokes. All this have empowered the sport statisticians to a further degree and they are able to handle massive amounts of data and explore the data in a substantially more systematic way.
Inferential statistics is based upon choosing and measuring the trustworthiness of conclusions about a population parameter based on information from a reduced portion of that population, which is a random sample. Among the many uses of inferential statistics, political predictions ar one 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 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. Clearly, the fundamental elements in inferential statistics are choosing which members of the general population will be polled and what questions will be asked. Imagine a situation 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 will Give their vote to 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, in some cases, the sampling procedure may have given rise to incorrect inferences. A classic example is 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. In fact, as history proves, 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 cast.
