Last updated on June 18th, 2025
Tautology is a fundamental concept when learning about logic and reasoning. It demonstrates whether a statement is true or false and is often represented using truth tables. In this topic, we are going to talk about tautology and show how truth tables are represented.
Tautology is a logical statement that is always true, irrespective of the truth values of its components. This is an important concept in propositional logic (which deals with statements that can be true or false). For e.g., the statement "it will either rain today or no," can be logically expressed as p ∨ ¬p. This statement will always be true, and therefore is a tautology.
In order to know more about tautology, we need to know the basic logical operations that are used to present compound statements. Take a look at the different symbols used in tautology statements.
Logic | Symbols | Representation |
AND | ∧ | A ∧ B |
OR | ∨ | A ∨ B |
NOT | ¬ | ¬ A |
If and only if | ⇔ | A⇔B |
Implies or if-then | → | A → B |
Is equivalent to | = | A = B |
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Making a truth table is mandatory to find out if a statement is a tautology or not. A truth table lists all the possible combinations of truth values and results in the truth value of the logical expression for each combination.
Let us consider a simple expression: P ∨ ¬P (P OR NOT P). The truth table will be:
P | ¬P | P ∨ ¬ P |
T | F | T |
F | T | T |
The statement P ∨ ¬ P is a tautology because it is true under all possible truth values.
Let us take another common example, (P ∧ Q) ∨ (¬P ∨ ¬Q):
P | Q | ¬P | ¬Q | P ∧ Q | ¬P ∨ ¬Q | (P ∧ Q) ∨ (¬P ∨ ¬Q) |
T | T | F | F | T | F | T |
T | F | F | T | F | T | T |
F | T | T | F | F | T | T |
F | F | T | T | F | T | T |
As you can see in the truth table, the statement (P ∧ Q) ∨ (¬P ∨ ¬Q) is a tautology because the statement is true in all possible combinations of P and Q.
Along with tautology, there are other statements that tells us whether a statement is false or true. Here, we will learn the difference between tautology, contingency, and contradiction.
Tautology | Contradiction | Contingency |
A statement that is always true regardless of the scenario | A contradiction is a statement that is always false regardless of the circumstances. | When a statement's truth value changes based on specific conditions. |
In a truth table, all values are true (T) | All rows in the truth table are false (F) | In this truth table, some rows are true, and some are false |
It is either wet or not wet | It is wet and it is not wet | It is wet and it is cold or it is |
wet or it is cold | ||
P ∨ ¬P | P ∧ ¬P | P ∧ Q or P ∨ Q (P or Q) |
Learning tautology can get tricky, especially for beginners. However, learning about some common mistakes and ways to avoid them can go a long way in mastering tautology. So let us take a look at some of the common mistakes:
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Is the statement P ∨ ¬P a tautology?
Yes, it is a tautology
The statement means P OR NOT P.
If P is true, P ∨ ¬P = T ∨ F = True.
If P is false, P ∨ ¬P = F ∨ T = True.
Since it is always true, it is a tautology.
Is ¬(P ∧ ¬P) a tautology?
Yes, this statement is a tautology.
The expression means NOT P AND NOT P.
If P is true, then ¬P is false, making P ∧ ¬P = False.
If P is false, then ¬P is true, making P ∧ ¬P = False.
In both cases, ¬(P ∧ ¬P) = True.
Is the statement P → (P ∨ Q) a tautology?
Yes, it is a tautology
P → (P ∨ Q) means "If P is true, then P OR Q is true.
If P = True, then P ∨ Q = True, so the statement is true.
If P = False, then an implication (False → anything) is always true.
Since all cases are true, the statement is a tautology.
Is P ∨ P a tautology?
No, it is not a tautology
P ∨ P = P, meaning it depends on the truth value of P.
If P is false, P ∨ P = False.
Since it is not always true, it is not a tautology.
Is (P ∧ (P ∨ Q)) ↔ P a tautology?
Yes, it is a tautology.
P ∧ (P ∨ Q) simplifies to P, because if P is true, then the whole expression is P.
The biconditional (↔) checks for equality, and both sides are always equal.
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Jaipreet Kour Wazir is a data wizard with over 5 years of expertise in simplifying complex data concepts. From crunching numbers to crafting insightful visualizations, she turns raw data into compelling stories. Her journey from analytics to education ref
: She compares datasets to puzzle games—the more you play with them, the clearer the picture becomes!