Descriptive Statistics, Inferential Statistics - Admission/Application Essay Example

Statistics Introduction Statistics is the study of learning from data, and for measuring, communicating uncertainty and controlling, and it thereby gives the navigation significances for controlling the scientific and societal advances course. Individuals working with statistics use statistical methods and thinking to a broad range of social, scientific, and business endeavors in such areas as biology, economics, astronomy, education, marketing, engineering, psychology, genetics, marketing, sports, public health and many others. The best idea concerning a statistician is that an individual gets to play in the backyard of every other individual. Most of the economics, military, political and social decisions, cannot be implemented without statistical formulas, such as the experiments design gain federal approval of a presently manufactured drug. This paper will analyze the various applications of each of the course elements in elaborating and making decisions concerning data. Descriptive statistics Descriptive statistics is used to illustrate the fundamental aspects of the data in a given study. These statistics provide simple summaries concerning the sample and the measures. When these statistics are combined with simple graphics analysis, they form the virtual basis of every quantitative data analysis. With descriptive statistics, an individual is merely describing what is or what the data displays. These statistics are applied to present quantitative descriptions in a manageable manner. In a research study, people may have many measures. Alternatively, they may measure a large number of individuals on any measure. Descriptive statics assists statisticians to simply massive amounts of detail a sensible manner. Each descriptive statistics reduces most of the data in a simpler summary. For example, considering a pure number used to analyze how good a batter is working in baseball, the batting average. The single number is simply the number of hits divided by the times at bat. Every moment an individual attempts to elaborate an enormous observation set of with a single indicator he or she runs the distorting risk of the original data or losing significant information. The average of batting does not inform the individual whether the batter is hitting home runs or singles. It does not inform if a person has been on a stripe or in a slump. The GPA does not provide information to the individual whether the student was in challenging courses or those that are easy or whether they were courses in their primary field or other disciplinary areas. Even provided these demerits, descriptive statistics give a significant summary that may enable comparisons between individuals or other forms of units (Chrisman, 1998). Inferential statistics In regards to inferential statistics, an individual is applying effort to come to conclusions that exceed the immediate data alone. For example, people use inferential statistics to try ti infer from the sample data the population might think. In addition, inferential statistics may be used to implement judgments of the probability that an observed dissimilarity between groups is a dependable one or one that may have happened by chance in this study. Therefore, individuals make use of inferential statistics directly to analyze what is going on in their data. Mostly, one of the simplest inferential test applies when a person wants to compare the average performance of two dissimilar groups concerning a single measure to examine if there is a dissimilarity. A person might wish to know whether eight-grade boys and girls have differences in math test scores, for instance, or whether a program group has dissimilarity on the outcome measure from a control group. Whenever an individual tries to compare the average performance between two groups he or she should consider the t-test for dissimilarities between groups. Most of the primary inferential statistics emerge from general statistical models commonly regarded as the General Linear Model. This comprises of the t-test, variance analysis, covariance analysis, analysis of regression, and most of the multivariate methods such as factor analysis, multidimensional scaling, discrimination function analysis, cluster analysis, and many others. Provided the significance of the general linear model, it is an important opinion for any serious social researcher to be familiar with its workings. The General Linear Model discussions are very significant here and only consider the simplest straight-line model. Nevertheless, it will get you familiar with the opinion of the linear model and assist in the preparation of the more sophisticated analyzes (Anderson et al. 1994). Hypothesis development and testing This includes the method of coming up with decisions using data obtained from an experiment, observational study or a survey, deciding whether the evidence is efficient to refuse status quo as elaborated by the null hypothesis. Parameter estimation does not explicitly comprise of making a decision. Rather individuals wish to determine the most plausible value or a range of the confidence interval. In contrast to that, the analysis of hypothesis testing objective is to decide which type of two competing claims is correct. In the literature of statistics, statistical hypothesis testing acts a significant role. The standard line of reasoning is that there is an initial research in regards to the assumption of which the truth is not identified. The first step considered is to determine the significant null and alternative hypothesis. This is of significance as mis-stating the hypotheses will muddy the following process. The second step is to put into consideration the statistical assumptions being implemented concerning the sample in performing the rest; for instance, assumptions regarding the statistical independence or concerning the outcomes of the test are invalid (Freedman, 2005). Most of the current statistics courses focus their attention on the techniques significant to perform statistical analysis and give the student samples for each statistical test. In similar manner, when an individual is performing research, the investigator should have a decision that the statistical test is significant for the data obtained. Due to statistics courses emphasis on performance of the tests rather the design of the study and how to select appropriate test, the investigator may experience challenges in choosing the appropriate test. Ironically, with the broad accessibility of hand-held calculators, microcomputers, and maybe user-friendly mainframe computers, performing comprehensive statistical tests is currently possible for almost all medical investigators. Therefore, the widest challenge lies in selecting the appropriate test (Stigler, 2001). Evaluating statistical results Analysis of statistics is a quantitative method to look for probabilities of sets or outcomes of data. This data can emerge from the natural or social sciences. Statistical analysis assists statisticians demonstrate on trends or patterns found in the topic’s research. For instance, if a medical doctor required testing the probable effectiveness of the drug, he, or she would use statistics to examine if the drug performed a particular number of times for a given population. Then, the doctor would illustrate the outcomes to examine if the drug outcomes have a relative or probable accuracy (Hand, 2004). In conclusion, statistics is a mathematical science section that consists of the collection, exploration or interpretation, analysis, and presentation of data, or as a mathematics branch. Some of the individuals consider statistics as a distinct mathematical science instead of a branch of mathematics. When applying statistics to various places of study, it is significant to start with a population or process to be studied. Populations can be broad topics that may compose of ‘all individuals living in a country’ or ‘every atom involving a crystal’. Ideally, every statistician compiles data concerning his entire population. References Anderson, Sweeney, Williams, (1994). Introduction to Statistics: Concepts and Applications, West Group. Chrisman, N. (1998). Rethinking Levels of Measurement for Cartography. Cartography and Geographic Information Science, 25 (4), 231–242. Freedman, D.A. (2005) Statistical Models: Theory and Practice. Cambridge University Press. Hand, D. J. (2004). Measurement theory and practice: The world through quantification. London, UK: Arnold,86-93. Stigler, S. M. (2001). "Francis Galtons Account of the Invention of Correlation". Statistical Science 4 (2); 73–79. Read More