Advertisers and companies that sell products try to convince their prospective customers that a product or service is better than it actually is. In order to do this, the brand managers use appealing statistics to sway the opinions of their customers. The most evasive statistic measures the average of a group. The average can be used to represent mediocrity. The problem is that the average can be severely skewed without the customer ever knowing.
For instance, consider a real estate agent that is trying to sell a house to a couple who just moved into town. The agent will claim that the average income of a family that lives in the neighborhood is $150,000. The couple builds an impression of the neighborhood based on a statistic that is completely wrong.
The real estate agent computed the average by adding up family incomes in the neighborhood and dividing by the number of families. The statistic seems harmless at the beginning, but changes once the data is analyzed more closely.
The family incomes have a high variability and are not consistent within the neighborhood. Suppose that there are five families out of twenty-five that earn $500,000. The five family incomes significantly skew the average and hold up the families that make less than $150,000. After the five high family incomes are taken out of the group, the average is only around $62,500.
The real estate agent tries to use prestige to lure the purchasers into buying a house in a neighborhood that is not depicted in the correct way. A more appropriate statistic to convey the actual representation of family income in a neighborhood is the median. To calculate the median, the family incomes are listed from lowest to highest. The family income that is in the middle is the median. This eliminates the risk of outliers and provides an accurate portrayal of family incomes in the neighborhood.
Family incomes are not the only group of data that is skewed to eliminate the reliability of the average statistic. Prices of goods in a basket, height, and wealth are all susceptible to having outliers that impact the average. To further your knowledge and lessen your risk of being deceived by statistics, I recommend reading How to Lie with Statistics by Darrell Huff. He provides excellent examples of bias, misleading graphs, and ways advertisers massage numbers. Statistics are a powerful tool that can be used dubiously with a little bit of creativity.