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Central tendency is the measure of the center or middle of a distribution of data. Central tendency can refer to a wide variety of measures but the most common one is the mean. Mean is called the average and is found but dividing the sum of the data by the number of data used. The mean is a good gauge for central tendency, but is can be misleading by skewed distributions. This can be greatly influenced by low or high samples in the tail of the distribution. In these instances, median may be a more accurate measure for the distribution. Median is the middle of a distribution; half the sample is above the media and half is below the median. A distribution can be positive, negative, or symmetric. Positive skewed distributions have a longer tail in the positive direction and negative would have a longer tail in the negative direction. Symmetric distributions have no skew and are a normal distribution. Learning Team E will be calculating the central tendency measures for the selected nations based on population size, people aged 65 or older, and life expectancy. Team E has chosen seven nations; China, Iran, Iraq, Japan, Russia, Saudi Arabia, and the United States to use as samples to find the central tendency.
First, Team E will explore population size for these nations (all numbers presented represent 1000’s). The population among the nations totaled together equals 1,935,629. Taking this number and dividing it by the number of nations in the sample gives a mean of 276,518. The median for the data is Japan’s population of 126,771. Because China has a very large population (1,273,111) the mean is affected and will show a positive skewed distribution that may not accurately present the data in the sample. The median shows a closer representation of the population size among these nations.
Next Team E will examine the population of people aged 65 or older in the sample

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