False connection and misinterpreted content
Wrongly interpreted, explained or incorrectly understood content - with or without intention
Can become disinformation when the receiver of the information doesn’t notice or isn’t aware of the error and as a result believes something in a wrong way
Wrongly interpreted statistics - misuse of numerical data
Data and statistics are often misinterpreted in one or more of the following ways:
- misleading data visualisation (also check this excellent data visualisation catalogue)
- wrong correlation (remember that correlation does not mean causation)
- data fishing (also known as data dredging)
- using percentages instead of amounts (in case of small sample size)
We recommend that you watch the Ted-Ed's “How statistics can be misleading" video by Mark Liddell (currently available with subtitles in 25 languages).
U.S. public health officials have been combating misconceptions about vaccine safety for over twenty years. They’ve had mixed success. Despite the fact that numerous studies have found no evidence to support the notion that vaccines cause autism and other chronic illnesses, a growing number of parents are refusing to vaccinate their children. Misconceptions arise - int his particular case - because there is a poor general understanding of how vaccines work. For example, This article provides a summary of the most common misconceptions related to vaccination:
> The “Overloaded Immune System” Misconception
> The “Disappeared Diseases” Misconception
> The “More Vaccinated Than Unvaccinated People Get Sick” Misconception
> The “Hygiene and Better Nutrition Are Responsible for the Reduction in Disease Rates, Not Vaccination” Misconception
> The “Natural Immunity Is Better Than Vaccine-acquired Immunity” Misconception
The lessons of famous science frauds is an article shedding light on two cases in which scientists have failed the standards.
Michael LaCour was a promising young social scientist until his eye-catching study about swaying public opinion on gay marriage, published last year in one of the world’s leading journals, turned out to have been built on data that can’t be found.
Anil Potti was a rising star at Duke whose studies of cancer genetics drew heaps of praise — and research dollars — until his academic career crumbled under questions about his résumé, and the integrity of his findings.”