Abstract
The mean for Z scoresare of better advantage when it comes to portraying the proportions under the curve. This is because they extend over to the decimals as a way of dealing with multiple data with varying values(Kamphaus 2005). The massive data is broken down resulting to compressed outcomes. As z scores are denoted in decimals, they can be converted to percentages to simply the data in a clarified manner. Z-scores thusbring out away of organizing data, to enhance better results and simplified facts,that there can be comparisons to bring out the variations between the multiple things.However, the fact that one may go astray due to the use of standard deviations is not true. Standard deviations are used to check for variance between two things or more hence it is not possible to make comparison between z scores and standard deviation.
The differences in means are established, and the distribution of the information is well displayed under the curve(Griffith 2010). Using the z transformation to compare and contrast data allows evaluations from which conclusions can be drawn. Therefore transforming data to conform to standard normal distribution comes out as a very important tool for organizing and decision making(Griffith 2010).
Zscores display that which is above or below the mean thus giving room for accuracy when determining the probability of generalized information (Campbell 2013). By studying the standard normal curve, that, which is below average, falls to the left hand sidewhile that, which is above average, lies on the right hand side (Campbell 2013). This transformation of multiple data to standard normal distribution hence comes out as a very significant role accomplished by Z scores(Campbell 2013).
As z scores are convertible to percentages, which, establishes a level of ranking from the general population (Campbell 2013). Once converted to percentages, it is easy to analyze the ranking of the population based on the location of a percentage score.
Z scores represent the individual performance while the percentages give information about the group as a whole. Both of these statistics can be deduced from the area under the curve. It is, therefore, in order to conclude that one does right to represent information as area under the curve(Campbell 2013).
References
Campbell W. (2013).Essentials of Electrodiagnostic Medicine.New York: Demos Medical
Griffith A. D. (2010).Spatial Correlation and Spatial Filtering: Gaining understanding through Theory and Scientific Visualization (Advances in visual Science) U.S.A: Springer.
Kamphaus W. R. (2005). 2nd ed. Clinical assessment of Child and Adolescent Intelligence.U.S.A: Springer.
