False causality is the false assumption that when two events takes place together, one event must have caused the other.
Example:
The two events may not be related at all but correlate by chance or by an unknown third factor.
Intuitively, the assumption does not make much sense. The correlation in this scenario could be due to this third factor – hot weather.
In this scenario, it is summertime in the USA. Because the weather is very hot, more people will buy ice cream to cool down. And because it’s summertime, more people will go to the beach and swim. And because there are more swimmers, the incidences of drowning increases. Therefore, the increase in ice cream sales does not cause the increment of drowning deaths.
Cherry picking is the selective reporting of data to support a hypothesis or argument.
This video illustrates how commonly it happens, and what to look out for in spotting misleading visualisations.
How well can you spot a misleading graph? Take this quiz to find out.
This video illustrates other ways data can be misrepresented in visualisations.
