This post is part of the conference on "Truth in numbers: the role of data in a world of fact, fiction and everything in between"
With the abundance of fake news and the spread of misinformation, it is critical for citizens to be able to identify real facts and data.
Take the following scenario. How many times have you heard "you have to wait 30 minutes to swim after eating"? While nearly universally understood as a general rule, where does this information and data come from? And what would happen if you eat before swimming?
Research shows that there is no proven evidence of the negative effects of eating before swimming*. While some might claim that digestion diverts blood flow, doctors say you have more than enough blood to fuel your muscles and digest your food. In fact, "accidents" while swimming are likely contributed to other factors such as alcohol use, lack of child supervision, and general inability to swim.
While swimming after eating may seem trivial, what about a more serious issue of misused data? One striking real-life example deals with vaccinations. In the late 1990s, a paper was published in a reputable medical journal claiming an association between the measles (MMR) vaccination and autism, which resulted in a backlash against childhood vaccines. However, follow-up studies by health experts, including the WHO, have since discredited the claim, being unable to find an established link between vaccinations and autism. The original author has also been exposed for falsifying data and seeking to profit from his anti-MMR vaccine research. Despite this evidence, anti-vaccination rhetoric is still strong. In 2002, between 20% and 25% of the public continued to believe in the link between vaccinations and autism. In result, vaccination rates in Europe have dropped and measles infections have quadrupled from 2016 to 2017.
While this last example demonstrates the very dangerous effects of misused (and false) data, both examples share one thing in common. Both myths are compounded by the spread of misinformation. If you dig deeper, you'll find that the data simply does not back up the original claim.
Looking into the data, how can you avoid these kinds of "traps"?
3 ways to avoid data traps
1. Check your pre-conceived ideas
- Does the number confirm or challenge your existing beliefs?
2. Understand the context
- Why is this number being used and what facts are missing?
3. Be aware of the source
- Can the data source be trusted?
*More information on the "30-minute rule"
This article was co-written by PARIS21 and the Swiss Federal Statistical Office.