Some books define measures of central tendency as measures of the central position. When applying the use of these measures of central tendency, some measures prove to be more effective and appropriate than others (Sharpe, 10). When applying the measures of central tendency one has to know the appropriate measure to use and under what condition is it suitable to work. There are three measures of central tendency, which includes the mean, mode, and median. Mean is the arithmetic average of a given set of data. Mean is the division of the sum of all available data by the number of all observations that contribute to the total sum of the data. Mean is summarizes the ratio or interval data in situations where the given set of data is symmetrical. The total frequency within each class interval concentrates at the midpoint of a given class interval (Davies, 21). To calculate the mean of a set of variables, one needs to divide the sum of all observations by the total number of the given observations. The reasons for this procedure are that the total value is dependent on two factors that are values of individual observations and the total number of observation. When determining the mean of a given set of data, one has to standardize the simple sum by evenly distributing it across all observations. The formula for calculating mean is. Mean= Total sum/total observations. Mean has several advantages as a measure of central tendency since it takes all values of the observation into account. Also, mean is unique since each set of data has its own unique mean. However, mean is susceptible to influence from other outliners. Unusual values that are numerically large or small in a given set of data can affect the outcome of mean (Davies, 22). Additionally, the mean method is not appropriate when measuring the central position of skewed data. In case of skewed data, the mean losses the ability to show the central position of the data because of the skewnessalways drags it from the typical value.