Logical Reasoning Scholarship Exam Prep
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Questions and Answers

Which of the following conclusions can be drawn if all cats are mammals and some mammals are dogs?

  • All dogs are cats.
  • No cats are dogs.
  • Some mammals are not cats. (correct)
  • Some cats are dogs.

If the following statements are true: 'All humans need water' and 'Some humans are athletes', which of the following must also be true?

  • Some athletes do not need water.
  • Not all humans are athletes.
  • Some humans do not need water.
  • All athletes need water. (correct)

If it is known that 'No rectangles are circles,' what can be inferred about squares?

  • No squares are circles. (correct)
  • All squares are rectangles.
  • Some circles are squares.
  • Some squares are circles.

In a certain dataset, if '80% of the participants are women' and '20% of the participants are men', which of the following is correct?

<p>The dataset contains more women than men. (B)</p> Signup and view all the answers

If every car must utilize a tire, which statement is necessarily true?

<p>Cars that are not using tires are not cars. (A)</p> Signup and view all the answers

Flashcards

Some mammals are not cats.

If a category (mammals) has a subset (cats), and another category (dogs) partially overlaps with that category, then there must be members of the larger category (mammals) that are not in the smaller category (cats).

All athletes need water.

If a property (needing water) applies to all members of a category (humans) and another category (athletes) is a subset of the first category, then the property must also apply to all members of the subset (athletes).

No squares are circles.

If two categories (rectangles and circles) have no members in common (no overlap), then any subset of one category (squares) must also have no members in common with the other category (circles).

The dataset contains more women than men.

If a dataset has a larger proportion of one category (women) compared to another category (men), then there must be more members of the larger category in the dataset.

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Cars that are not using tires are not cars.

If a characteristic (using a tire) is necessary for something to be considered within a category (cars), then anything lacking that characteristic cannot be considered within the category.

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