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Description: The general beliefs that we use to categorize people, objects, and events while assuming those beliefs are accurate generalizations of the whole group.

Logical Form:

All X’s have the property Y (this being a characterization, not a fact).

Z  is an X.

Therefore, Z has the property Y.

Example #1:

French people are great at kissing.  Julie is French.  Get me a date!

Explanation: “French people are great at kissing” is a stereotype, and believing this to be so is a fallacy.  While it may be the case that some or even most are great at kissing, we cannot assume this without valid reasons.

Example #2:

Atheists are morally bankrupt.

Explanation: This isn’t an argument, but just an assertion, one not even based on any kind of facts.  Stereotypes such as these usually arise from prejudice, ignorance, jealousy, or even hatred.

Exception: Statistical data can reveal properties of a group that are more common than in other groups, which can affect the probability of any individual member of the group having that property, but we can never assume that all members of the group have that property.

Tip: Remember that people are individuals above being members of groups or categories.


MAC, M. J. T., PhD, CSAC. (2006). Critical Thinking for Addiction Professionals. Springer Publishing Company.

Registered User Comments

Wolfgang P
Thursday, March 23, 2017 - 12:16:26 PM
This is a fallacy I'll gladly commit. If 90% of green-haired people are murderers compared to 1% of all others, I will shun ALL green-haired people, knowing full well that I do injustice to 10% of them.
(Note: Numbers and hair color are pulled out of thin air to illustrate.)

With the right data and wording, this can be turned into a valid argument. Example #1: "90% of French people are great at kissing. Julie is French. Therefore, I have a high likelihood of encountering a great kisser, so I agree to a date." However, this argument still leaves out the possibility that Germans may have a 95% chance of the same, indicating that I should date Petra instead. Or both for even better odds? lol

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Bo Bennett, PhD
Thursday, March 23, 2017 - 12:59:16 PM
The fallacy is in the confidence of the assumption compared to the data. For example, if 90% of green-haired people are murders, and you meet Jack, a green-haired fella, you are reasonable in assuming that, given no other information, there is a 90% chance that Jack is a murderer. How you choose to treat 100% of green-haired people is a moral choice, outside of reason and logic. Your "shunning them all point" might be 90%, but for some people, that percentage is much lower (like .00664 percent, and instead of green-hair, it is dark skin).

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