What are ways that statistical information can be misleading?

Let’s look at some examples of how statistical information can be misleading in everyday life.  

  1. Advertisements: Sometimes, advertisements use “relative risk” to make their products appear more effective than they are. For example, an ad might say that a weight loss supplement can help you lose weight “twice as fast” as dieting alone. While this may sound impressive, knowing the actual numbers behind the claim is essential. If the average weight loss with dieting alone is only 1 pound per month, then losing 2 pounds per month with the supplement might not be as significant as it first appears.
  2. News Reports: News reports often focus on attention-grabbing headlines and may not provide the full context. For instance, a news report might say that “crime rates have increased by 50% in the past year.” While this may sound alarming, it’s important to consider the base rate. If the crime rate was extremely low, to begin with, a 50% increase might not be as significant as it sounds. Additionally, it’s crucial to consider other factors, such as population growth or changes in reporting methods, which might have contributed to the increase.
  3. Social Media Reports: Online polls or surveys shared on social media often suffer from sampling bias. For example, a blogger might post a poll asking their followers if they prefer cats or dogs. The results may show that 80% of respondents prefer cats, but it’s important to remember that the sample is limited to the blogger’s followers and may not be representative of the general population.
  4. Customer Ratings: Online customer ratings can be influenced by various factors, such as selection bias and the “halo effect.” People who have had exceptionally positive or negative experiences may be more likely to leave a review, skewing the average rating. Additionally, a person’s perception of one aspect of a product or service can influence their ratings of other aspects. For example, if someone has a positive experience with a restaurant’s service, they might be more likely to rate the food highly as well.
  5. Health Tracking: Fitness trackers and health apps often use statistics to show progress and encourage users to meet specific goals. However, these statistics can sometimes be misleading. For example, a step counter might encourage users to take 10,000 steps per day, but this number is arbitrary and might not be suitable for everyone. When interpreting health statistics, it’s essential to consider individual factors, such as age, fitness level, and personal goals. 


It’s important to approach statistical information with a critical eye, considering the context and potential biases when interpreting the numbers. By doing so, you’ll be better equipped to make informed decisions in your everyday life.