Statistics is feared by many students and corporate executives. They fear the numbers and how the numbers are manipulated and used to prove or disprove some concept or event. The fat is that statistical analysis is fairly easily and only takes a brief understanding of how the differing types of statistical functions work in any given situation. The definition of statistics is an explanatory type of mathematics that organizes, predicts, analyzes and defines the numbers that are representative of the population being studied (Aron, Aron, & Coups, 2006; statistics, 2009).
In fact it is the statistical power of the analysis of research data that is the basis for any type of statically significant data and its representation of the population. This analysis of the population data is what helps researchers and scientists to better understand behavior and other psychological phenomena (Aron, et al, 2006; Petocz, & Reid, 2005). There are two main ways to decipher statistical information. The first is called inferential statistics.
Statistically speaking the concept of inference is the explanation of study data that is representative to the population (Brewer, 2005; inference, 2009). When using inferential statistics then one is using procedures to infer that a population is or is not represented by the data collected. The second path of statistics is differential statistics which is based on finding the differences in a population using quantitative or numerical data (differential, 2009; Fidalgo, Hashimoto, Bartram, & Muniz, 2007).
Basing all of the knowledge of statistics on either inferences about a population or the differences between populations is the foundation of all statistical function. The use of statistics is really just a different way to understand people and psychological events and how external factors influence those predicted behaviors or different actions. The role of statistics is now greater than ever before, and is much easier to truly understand when it is based on real life data.