Central Tendency Measures
The Central tendency has been used as in evaluation and statistics in determining the mean, medium and mode. The Accountant uses a central tendency to evaluate most of the employees when they apply a rating scale. There is be a trend for salaries to fall within a calculated mean that does not be a representation of sampled data due to its sensitivity to extreme values (Devore, 2015).
This can be problematic since lower remunerated employees may be appraised slightly above mean. The rating may not be correct because the highly paid employee such as the CEO is rated in the same range, he deserves better rating. The limitation of the rating scales tend to cause less central tendency bias, but they also become less exact because there are stretched data figures from the sampled staff. The median is appropriate since it is not affected by outliers because skewed data cannot distort it or affected by changes in data away from the center (Pineda, Aguilar, Axotla, León, and García, 2013).
Mean finds the most accurate average of the set of numbers. It is appropriate for calculating the average salaries, working with data sets of independent values taken at one point in time. However, it is not suitable for the time series type of data. It is affected by outliers of $ $220,000 and $ 25,000 that impacts the mean a lot, making it much lower or higher than it should be. The gap between the CEO and the receptionists’ salaries is large; it can be small between other numbers in the data. However, it causes the mean/average, $ 80,714 to be a very inaccurate way to find the middle of a set of values. This is prompted by datasets containing few extreme values or with more dispersed data sets in the samples. If the returns are volatile, there will be a significant weakness in the arithmetic average (Gumbel, 2012). This renders the median a better alternative in computing the average salary for the staff in the organization.
Devore, J. (2015). Probability and Statistics for Engineering and the Sciences. Cengage Learning.
Gumbel, E. J. (2012). Statistics of extremes. Courier Corporation.
Pineda, M., Aguilar, A., Axotla, J. C., León, F., & García, O. (2013). Teaching the topic of Central measures Tendency and Variability through of e-learning course, in the class of Statistic in the higher faculty of Studies Cuautitlan. Edulearn13 Proceedings, …