share
Overconfident Inference from Unknown Statistics

source

## share

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 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, 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 knonw 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.

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.”

Example #3

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.”

Originally appeared on A Philosopher’s Blog Read More

## Don’t Do Unto Others As You Will

I recall, as a kid, watching one of the many episodes of the popular sitcom All In The Family. Edgy...

## Ask Yourself This Question Every Day

Jim Kwik is the type of person I typically would steer away from. He appears to have created an industry...

I heard a quote a couple of days ago that has stuck in my head and incessantly keeps repeating in...

## Hating Those Who Want To Open Up

A friend sent me this vitriolic, hate-filled rant against people that are moving to reopen the United States economy and...

## Institutional distrust in Britain and America: a history

In the past few decades, trust and distrust have become frequent subjects of journalistic and academic discourse. Much of this...

## The Theory of Two Truths in Tibet

[Revised entry by Sonam Thakchoe on May 28, 2022. Changes to: Main text, Bibliography] Tibetan philosophers argue that the two...

## “All rules for study are summed up in this one: learn only in order to create.”

“All rules for study are summed up in this one: learn only in order to create.” – Friedrich Schelling, On...

## “The philosophical concept was derived from the ordinary one through all sorts of misunderstandings,…”

“The philosophical concept was derived from the ordinary one through all sorts of misunderstandings, and it strengthens these misunderstandings. It...