Teacher Guide: Common Logical Fallacies
Learn to spot and fix the most common logical fallacies, from affirming the consequent to correlation vs causation, with math examples.
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Class quiz
10 questions on Logical Reasoning. Students join with a name, you see everyone's score.
For Teachers
- Distinguish valid argument forms from affirming the consequent and denying the antecedent
- Identify common informal fallacies in written arguments and explain the error
- Use a counterexample to show that a conclusion does not follow
- Recognize misused statistics: totals instead of rates, small samples and correlation presented as causation
- Rewrite a fallacious argument so that it becomes valid or honest
- • If-Then Statements: hypothesis, conclusion and the truth table of
- • Converse and Inverse: why a true statement can have a false converse
- • Contrapositive: the equivalent form
- • Percentages and rates, such as
- 1. If it rains, the street is wet. The street is wet. Did it rain? What else could have happened?
- 2. Can an argument with a fallacy still have a true conclusion? Give an example.
- 3. Find a headline that compares totals. What number would you need to make a fair comparison?
- 4. When is it reasonable to trust an expert, and when does it become an appeal to authority?
If the conclusion is true, the argument must be valid
Remediation: Show an invalid argument with a true conclusion: if a number is divisible by 6, then it is even. 12 is even, so 12 is divisible by 6. The conclusion is true, but the same form gives a false result for 14.
A strong correlation proves that one thing causes the other
Remediation: Use ice cream sales and sunburns: both rise in summer. Ask students to name the hidden third factor (sunny weather) before discussing controlled experiments.
If something worked many times, it will always work
Remediation: Let students compute with a calculator for , ask for a conjecture, then reveal and the factors . The surprise makes the point stick.
For Struggling Students:
- • Give a table with the four forms and let students match each argument to a row
- • Start with everyday arguments before moving to numbers
- • Provide the counterexample and ask students to explain why it breaks the argument
For On-Level Students:
- • Name the fallacy in ten short arguments and write a corrected version of each
- • Compute rates from news data and decide whether a headline is fair
- • Use the truth table of to explain why affirming the consequent fails
For Advanced Students:
- • Research a real statistical claim and identify the possible confounding variables
- • Use a calculator to find the next prime after 11 for which is not prime
- • Write a short debate speech that avoids every fallacy in the lesson, then let classmates check it
- MP3 (CCSS.MATH.PRACTICE.MP3)
Construct viable arguments and critique the reasoning of others
- HSS.ID.C.9 (CCSS.MATH.CONTENT.HSS.ID.C.9)
Distinguish between correlation and causation
- HSS.IC.B.6 (CCSS.MATH.CONTENT.HSS.IC.B.6)
Evaluate reports based on data
- visualInteractive Truth Table
Students find the row of where both premises are true but is false
- activityFallacy Detectives
Groups collect ads and headlines, name the fallacy and rewrite the claim honestly
- worksheetValid or Fallacy?
Sort 12 arguments into modus ponens, modus tollens, affirming the consequent and denying the antecedent
Lesson Content
Everything students see: definition, examples, common mistakes, applications. Tap to open.
Lesson Content
Everything students see: definition, examples, common mistakes, applications. Tap to open.
Definition
- Ad hominem: attacking the person instead of the argument.
- Straw man: changing the other person's claim into a weaker one, then attacking that.
- False dilemma: offering only two options when there are more.
- Hasty generalization: a general rule from too few cases.
- Circular reasoning: the conclusion is hidden in the premises.
- Slippery slope: claiming one small step must lead to a disaster.
- Appeal to authority: true because a famous or powerful person said so.
- Correlation vs causation: two things happen together, so one must cause the other.
Worked Examples
Argument: If a number is divisible by 6, then it is even. 14 is even. Therefore 14 is divisible by 6. Is the argument valid?
Name the parts
: the number is divisible by 6. : the number is even. → Rule:
Find which part is given
We are told that 14 is even, so the conclusion is given, and the argument concludes . → Form: , therefore
Compare with the valid forms
Modus ponens needs as a premise, not . This is affirming the consequent. → Invalid form
Check with a calculation
remainder → 14 is not divisible by 6
Answer: The argument is invalid: it affirms the consequent. 14 is even but not divisible by 6, so the conclusion is even false here.
Common Mistakes
Thinking that a fallacy proves the conclusion is false
Why it's wrong: A fallacy only shows that the argument does not prove its conclusion. Take the rule if , then . Concluding from is invalid, yet might still be .
Correct: Say the conclusion does not follow. To show it is false you need a counterexample, here .
Confusing affirming the consequent with modus ponens
Why it's wrong: Both use and look alike. The difference is which part is given as a premise.
Correct: Given , you may conclude (valid). Given , you may not conclude (affirming the consequent).
Treating many examples as a proof
Why it's wrong: Checking 3 or even 100 cases is still a hasty generalization if the claim is about all numbers.
Correct: Use examples to make a conjecture, then prove it with an argument that covers every case, or find a counterexample.
Calling every mention of an expert an appeal to authority
Why it's wrong: Citing a relevant expert together with evidence is normal. The fallacy is using a name instead of evidence, especially outside the person's field.
Correct: Ask: is this person an expert on this exact topic, and what evidence supports the claim?
Why It Matters
- Mathematics: Checking a formula for 4 cases is not a proof. For the primes the number is prime, but for it gives .
- News and statistics: Headlines often turn a correlation into a cause, or report big totals without dividing by the population.
- Advertising: 8 out of 10 users liked it! sounds strong until you learn that only 10 people were asked.
- Debates and social media: Ad hominem attacks and straw men replace real arguments, and false dilemmas hide better options.
- Science: Researchers use control groups precisely because correlation is not causation.
Real World Applications
Advertising Claims
Ads love percentages and famous faces. A small sample and a celebrity are a hasty generalization and an appeal to authority working together.
Example:
An ad says: 80% of users got better grades, says a famous football player! The player is not an expert on learning, and the 80% may come from very few people.
A study app has 5000 users. The ad claim of 80% comes from a survey of 10 users, and 8 of them answered yes.
What percentage of all users were actually asked?
Step 1: Write the mathematical expression
Write the share of users who were asked, as a percentage:
News Headlines and Totals
Headlines often compare totals without dividing by the size of the group. Big cities have big totals of almost everything.
Example:
City A has 4000 deaths a year and City B only 300, so a headline says City A is far more dangerous. But City A has 2000000 people and City B has 100000.
City A: 2000000 people, 4000 deaths a year. City B: 100000 people, 300 deaths a year. Compare the rates per 1000 people.
Which city has the higher death rate per 1000 people?
Step 1: Write the mathematical expression
Write the rate per 1000 people for City A:
Road Safety Statistics
A famous claim says most accidents happen close to home, so driving near home is dangerous. It ignores the base rate: how much driving happens near home.
Example:
If 85% of all kilometers are driven near home, we would expect about 85% of accidents there even if the risk were the same everywhere.
In a region, 700 of 1000 accidents happen within 10 km of home, and 85% of all driving happens within 10 km of home.
Does the data show that driving near home is more dangerous?
Step 1: Write the mathematical expression
Write the number of accidents expected near home if the risk were the same everywhere:
Key Takeaways
- 1A fallacy is an error in reasoning: the conclusion does not follow from the premises
- 2Affirming the consequent (, , so ) and denying the antecedent (, , so ) are invalid forms
- 3Modus ponens (, , so ) and modus tollens (, , so ) are the valid forms
- 4Informal fallacies include ad hominem, straw man, false dilemma, hasty generalization, circular reasoning, slippery slope and appeal to authority
- 5Correlation is not causation: look for a third factor and compare rates, not totals
- 6Examples never prove a general rule, but one counterexample disproves it
Frequently Asked Questions
If an argument has a fallacy, is its conclusion wrong?
What is the difference between a formal and an informal fallacy?
How many cases do I need to check to prove a rule?
How can I tell correlation from causation?
Glossary
- Fallacy
- An error in reasoning that makes an argument fail to prove its conclusion
- Formal fallacy
- An error in the form of an argument, such as affirming the consequent
- Informal fallacy
- An error in the content of an argument, such as a straw man or a false dilemma
- Affirming the consequent
- The invalid form , , therefore
- Denying the antecedent
- The invalid form , , therefore
- Confounding variable
- A hidden third factor that affects two things and makes them look connected
Formula Card
Modus ponens (valid)
The hypothesis is given, so the conclusion follows
Modus tollens (valid)
The conclusion is false, so the hypothesis is false
Affirming the consequent (invalid)
The converse error: $q$ can be true while $p$ is false
Denying the antecedent (invalid)
The inverse error: $q$ can be true while $p$ is false