Statistical Analysis

Statistical analysis is normally referred to as a collection of methods that are used in processing large amount of information or data and also report the overall trends. Therefore, it is mainly useful when dealing with specific data. It provides different ways of reporting on how unusual event is actually based on certain historical data. For instance, our server normally uses different statistical analysis in order to examine tremendous amount of data that is produced everyday by stock market. Therefore, people prefer statistical analysis to other traditional forms of the technical analysis.

There are two different types of statistical analysis this include: descriptive and inferential statistics. Therefore, the major goal of this paper is to differentiate between two main types. To start with, descriptive statistics usually corresponds to the act of defining different characteristics of a given statistical measurement. Hence, it is based upon the methods and mechanisms that are employed to summarize and organize any raw data. So as to categorize that data from the random sample collected, many statisticians uses charts, tables, graphs and standard measurements like measurements of variation, average and percentiles.

There are many ways in which this type of statistic has been used for instance, in baseball. Statisticians spend a lot of effort and also time examining and summarizing data they usually get from the game. For instance in 1948, over six hundred games were played in the league of America. Therefore, so as to determine which team that had best batting average, a lot of effort was required. This is because they were required to take official scores for every game, make a list of each batter, then compute all the results of each, add total number of the hits made and also total number of different times at bat so as to calculate with the batting average. This proved to be a lot of work and more complicated.

Nevertheless, due to technology this has changed a lot. This is because the use of various computer statistical programs together with capability of incorporating statistical functions on the spreadsheet programs like excel shows that more detailed and complicated information can actually be collected, formatted and also presented with just a couple of key strokes. As result all these has made many statisticians to handle a lot of data and explore it in a systematic way with a short duration.

The second type is inferential statistics. It is mostly based upon measuring and choosing trustworthiness of the conclusion about certain population parameter that is based on information from random sample which is the reduced portion of the same population. One good example where inferential statistic applied is the political predictions. For instance, you find that in order to predict who will actually be the winner in an election like presidential election, sample of few thousand who are carefully chosen are asked whom they are going to vote.

Therefore, from the answers they end up giving, statisticians are able to infer or predict who will be voted in. Without doubt, the primary elements in this type are choosing general population that will be polled and the questions that they will be asked. Hence, inferential statistics highly relies on the results. Therefore it is easier to predict who will win the election. On the other hand, the sampling may sometimes give rise to inferences that are incorrect. Therefore many statisticians have tried to look for other ways of collecting data.

In conclusion, these two types are quite important in data collection. However, many people prefer using descriptive to inferential statistics this is because its results are more accurate.