Cherry Picking Statistics
Statistical cherry picking is when someone reports only information that will support a specific point of view. This involves omitting pertinent data which either contradicts the intended finding, or suggests that it may not be as robust as desired. For instance, to make a news article "pop", a reporter might mention a key finding from a journal article, despite the presence of other evidence that opposes their conclusions (resulting in it being clickbait). Another reason people cherry pick data is to artificially bolster their arguments. However, by doing this, they are committing a fallacy by setting up a strawman argument. This problem is an epidemic throughout the media, and even within academic research.
The reason statistical cherry picking is so appalling is that it paints a distorted picture of actual reality. This can be commonly observed via memes on social media. It is rampant in this context because it is easy to do, feels good, and works with our susceptibility to confirmation bias. This means people tend to post without verifying accuracy, so long as it lines up with their personal beliefs.
One might think that a meme or random article wouldn't do any harm; after all, it's just a silly article, blog post, or meme. However, this can be dangerous as people may use the information to make decisions. For instance, parents may decide not vaccinate their child due to cherry-picked statistical information, despite a preponderance of evidence that it is safe and effective. This has led to some of the worst outbreaks of controllable diseases in decades, many of which were previously thought to be eradicated in the United States.
The essential point is that one needs to be careful and responsible when reading and reporting statistics. Make sure to provide as complete a picture as possible around the subject matter. Finally, always question whether the full picture was provided before using cherry-picked information to make a decision that could impact both yourself and others.