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CSS 2026General Science & AbilityQ. 8

(a) Unscramble following high-level logical reasoning and research methodology terms: i) NALYLTCAIA ii) EFNRECNIE iii) OCNDIUTNI iv) NRBTASACIOT v) SHTESYPHOI. (b) Argument: 'Because many successful entrepreneurs dropped out of college, formal education is unnecessary for achieving high professional success.' Task: (i) Identify the fallacy / faulty reasoning in the statement. Explain how it affects the credibility of the argument. (ii) Narrate how verbal reasoning skills help detect and correct such flawed arguments. (c) A mechanical reasoning scale measures the ability to understand physical principles. Consider a system where two gears (Gear A and Gear B) are interlocked. Gear A has 12 teeth, and Gear B has 36 teeth. (i) Determine the rotation direction of Gear B if Gear A rotates clockwise. (ii) Compute how many complete rotations Gear A must make for Gear B to complete exactly 2 rotations. (iii) Explain how mechanical reasoning scales evaluate a test-taker's ability to generalize such mechanical principles to unfamiliar problems. (d) The average weight of 12 students in a group is 58 kg. When one new student joins the group, the new average becomes 60 kg. Calculate the weight of the new student, showing all steps clearly.

By CSS Nexus Team ·

Original CSS 2026 General Science & Ability paper

This answer to the CSS 2026 General Science & Ability paper (Q. 8) was written by the CSS Nexus team. Read the outline first, then see how each point is developed.

Outline

  • (a) ANALYTICAL, INFERENCE, INDUCTION, ABSTRACTION, HYPOTHESIS
  • (b) Hasty generalisation and survivorship bias; verbal reasoning
  • (c) Gears: anticlockwise; 6 rotations; mechanical reasoning scales
  • (d) 780 − 696 = 84 kg

Answer

(a) Unscrambled terms

i) NALYLTCAIA = ANALYTICAL ii) EFNRECNIE = INFERENCE iii) OCNDIUTNI = INDUCTION iv) NRBTASACIOT = ABSTRACTION v) SHTESYPHOI = HYPOTHESIS

(b) The flawed argument

(i) The fallacy: the argument commits a hasty generalisation, drawing a broad conclusion from a selected set of examples. It also shows survivorship bias, as it looks only at dropouts who succeeded and ignores the many who did not, and at successful graduates. It further confuses correlation with cause, since the success of those people may owe more to talent, capital, networks or timing than to leaving college. Finally, the claim that many entrepreneurs dropped out does not prove the leap that formal education is unnecessary for professional success generally, including in fields such as medicine and law, where it is required.

Effect on credibility: the evidence is anecdotal and one-sided, and it does not support so general a conclusion, so the argument is weak, however persuasive the famous examples sound.

(ii) How verbal reasoning helps: verbal reasoning trains a person to separate premises from conclusions, identify hidden assumptions, and test whether the evidence actually supports the claim. It draws attention to absolute or vague terms such as 'many' and 'unnecessary', and it prompts questions such as: how many, compared to whom, and what about those who failed? To correct the argument, one can narrow the claim and add evidence: 'Some people succeed without a degree, but formal education remains valuable, and required in many professions.'

(c) Gears and mechanical reasoning

(i) Direction: interlocked gears turn in opposite directions, so if Gear A rotates clockwise, Gear B rotates anticlockwise.

(ii) Rotations: the same number of teeth pass the contact point on both gears. For 2 rotations of Gear B, 2 × 36 = 72 teeth must pass. Gear A's rotations = 72 ÷ 12 = 6 complete rotations.

(iii) Mechanical reasoning scales: these tests present situations using gears, levers, pulleys and similar devices, and measure understanding of principles instead of memorised facts. They evaluate generalisation by placing familiar principles in new or more complex settings, such as chains of several gears, where a test-taker must trace direction and speed. The scores show whether a person can visualise movement, identify the relevant rule, and apply it to unfamiliar problems. They are therefore used for selection and for predicting success in technical training and engineering work.

(d) Weight of the new student

Total weight of 12 students = 12 × 58 = 696 kg.

Total weight of 13 students = 13 × 60 = 780 kg.

Weight of the new student = 780 − 696 = 84 kg.

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