Since I often reference various fallacies in blog posts I decided to also post the fallacies. These are from my book 110 Fallacies.
Description:
This fallacy is committed when a person places unwarranted confidence in drawing a conclusion from statistics that are unknown.
Premise 1: “Unknown” statistical data D is presented.
Conclusion: Claim C is drawn from D with greater confidence than D warrants.
Unknown statistical data is just that, statistical data that is unknown. This data is different from “data” that is simply made up because it has at least some foundation.
One type of unknown statistical data is when educated guesses are made based on limited available data. For example, when experts estimate the number of people who use illegal drugs, they are making an educated guess. As another example, when the number of total deaths in any war is reported, it is (at best) an educated guess because no one knows for sure exactly how many people have been killed.
Another common type of unknown statistical data is when it can only be gathered in ways that are likely to result in incomplete or inaccurate data. For example, statistical data about the number of people who have affairs is likely to be in this category. This is because people generally try to conceal their affairs.
Obviously, unknown statistical data is not good data. But drawing an inference from unknown data need not always be unreasonable or fallacious. This is because the error in the fallacy is being more confident in the conclusion than the unknown data warrants. If the confidence in the conclusion is proportional to the support provided by the evdience, then no fallacy would be committed.
For example, while the exact number of people killed during the war in Afghanistan will remain unknown, it is reasonable to infer from the known data that many people have died. As another example, while the exact number of people who do not pay their taxes is unknown, it is reasonable to infer that the government is losing some revenue because of this.
The error that makes this a fallacy is to place too much confidence in a conclusion drawn from unknown data. Or to be a bit more technical, to overestimate the strength of the argument based on statistical data that is not adequately known.
This is an error of reasoning because, obviously enough, a conclusion is being drawn that is not adequately justified by the premises. This fallacy can be committed in ignorance or intentionally committed.
Naturally, the way in which the statistical data is gathered also needs to be assessed to determine whether other errors have occurred, but that is another matter.
Defense: The main defense against this fallacy is to keep in mind that inferences drawn from unknown statistics need to be proportional to the quality of the evidence. The error, as noted above, is placing too much confidence in unknown statistics.
Sorting out exactly how much confidence can be placed in such statistics can be difficult, but it is wise to be wary of any such reasoning. This is especially true when the unknown statistics are being used by someone who is likely to be biased. That said, to simply reject claims because they are based on unknown statistics would also be an error.
Example #1
“Several American Muslims are known to be terrorists or at least terrorist supporters. As such, I estimate that there are hundreds of actual and thousands of potential Muslim-American terrorists. Based on this, I am certain that we are in grave danger from this large number of enemies within our own borders.”
Example #2
“Experts estimate that there are about 11 million illegal immigrants in the United States. While some people are not worried about this, consider the fact that the experts estimate that illegals make up about 5% of the total work force. This explains that percentage of American unemployment since these illegals are certainly stealing 5% of America’s jobs. Probably even more, since these lazy illegals often work multiple jobs.”
Example #3
Sally: “I just read an article about cheating.”
Jane: “How to do it?”
Sally: “No! It was about the number of men who cheat.”
Sasha: “So, what did it say?”
Sally: “Well, the author estimated that 40% of men cheat.”
Kelly: “Hmm, there are five of us here.”
Janet: “You know what that means…”
Sally: “Yes, two of our boyfriends are cheating on us. I always thought Bill and Sam had that look…”
Janet: “Hey! Bill would never cheat on me! I bet it is your man. He is always given me the eye!”
Sally: ‘What! I’ll kill him!”
Janet: “Calm down. I was just kidding. I mean, how can they know that 40% of men cheat? I’m sure none of the boys are cheating on us. Well, except maybe Sally’s man.”
Sally: “Hey!”
Example #4
“We can be sure that most, if not all, rich people cheat on their taxes. After all, the IRS has data showing that some rich people have been caught doing so. Not paying their fair share is exactly what the selfish rich would do.”