How Statistics Can Be Misleading: Two Real-World Examples

Introduction: The Misleading Nature of Statistics

Statistics can be misleading. People can use misleading statistics to persuade others to buy a product or to share a particular point of view. The following examples illustrate how this can happen.

Weight Loss Advertising and Atypical Results

Many ads for weight loss products include the following statement in small print beneath the product claims: "These results are not typical" (Bluman, 2011, p. 810). What does this say about the product being advertised?

To sell a weight loss product, a company needs to show that it can yield dramatic results. People who buy these products are often frustrated and desperate because other methods of weight loss have failed. The company will therefore showcase a result from the top of the range to entice buyers. An average or mean would be a lower number, which may not attract as much attention in advertising. The advertising shows what is possible, not what someone can necessarily hope to achieve.

When results are described as "not typical," it may also mean that the individual augmented the weight loss program in some way. For example, celebrities who endorse weight loss plans that deliver home meals may have also hired personal trainers and outfitted a home gym with expensive equipment — neither of which is within reach of the average dieter.

People may pay for weight loss products but fail to use them or use them incorrectly. This is why, as with gym memberships, payment in advance is often required rather than a pay-as-you-go model. Many people start a weight loss plan with enthusiasm but do not follow through. That is typical behavior, and it is not the behavior model that sells products.

Workplace Fatalities and Gender: A Closer Look

For a specific year, there were 6,067 male fatalities in the workplace and 521 female deaths. A government official made the following statement: "Over 90 percent of the fatal injuries the past year were men, although men accounted for only 54 percent of the nation's employment." Can we conclude that women are more careful on the job? (Bluman, 2011, p. 812).

One cannot conclude from this data that women are more careful on the job. Direct comparisons would need to account for the type of job and the workplace setting in which the fatalities occurred. In general, far more men than women work in potentially dangerous occupations — coal mining, logging, deep-sea fishing, power-line installation, and infantry service, among others. Fatality rates in these industries are significantly higher than in other fields. The fatality rate is therefore a function of the job, not the gender of the person performing it.

Although women are increasingly represented in fields once considered the exclusive domain of men, there remains a disproportionate number of women employed as teachers, nurses, and administrative assistants. Occupational fatality research consistently shows that these roles carry no inherent physical danger, which helps explain the disparity in workplace death statistics.

Conclusion: Reading Statistics Critically

As has been shown, facts can be misleading. In both examples, the data is attention-grabbing. Most people do not take the time to reflect on statistics and the claims they support. With some thought, however, one can see there is far more behind the numbers than appears at first glance. Understanding statistical inference and the context surrounding data is essential to drawing accurate conclusions.

Bluman, A. G. (2011). Mathematics in our world (1st ed. [University Custom]). McGraw-Hill.