How to Solve Tricky CAT 2025 DILR Logical Reasoning Sets

Data Interpretation and Logical Reasoning (DILR) section of the CAT sometimes presents sets with incomplete or inconsistent data, which can be challenging. These sets are meant to test your logical reasoning, not your plug-and-chug ability. For candidates, the frustrating part is normally "missing data." This document aims to assist you in developing a reasoned approach using logical reasoning, strategic set selection, and timing techniques.

What Are DI Sets with Missing Information?

DI sets with missing information are constructed to include gaps. Gaps compel candidates to reason instead of simply computing numeric information. Gaps can be represented as follows:

• Blank cells in a table
• Partial bar or pie chart values
• Totals or subtotals are not included
• Caselet variables are not reported

The focus isn't to fill in every blank, but to extract whatever the logic of the data allows to be solved. In CAT, while it is important to consider the time it takes to arrive at a "logical" answer, even with missing data, most question types have an answer.

Key Challenges in Solving Missing-Data DI Sets

• Formula methods fail when data is missing.
• Overdoing (or over-trying) to fill in lost data accounts for wasted time and reduced efficiency.
• The logic must replace direct computation—the candidates must be able to identify constraints, patterns, or relative relationships.

Core Strategies to Tackle DI Sets with Missing Data

First, highlight things that are known vs unknown.

• Keep track of totals, partial sums, or trends that connect data points.

Make groups of the set

• If working with a large table or chart, break it down into smaller groups.
• It is better to use analysis on one relationship at a time to prevent confusion.

Make logical ties and bounds

• Use totals, averages, or a ratio type analysis to deduce unknown values.
• For example, if you know three parts of a totality of a whole, you can deduce the last.

Use Variation Algorithms

• Sample unknowns as x, y, & z and set up the equations using these variables.
• Stick to the simplest equations—do not start making things complicated if you don't have to.

Eliminate Impossible Options

• In MCQ, just reject values that go against what you have been told.
• You do not need to fully determine an answer—you will actually avoid wasting time

Use Logic/Estimation

• If there is a set of answer choices that has a wide range, just make an approximation.
• You can use estimation whenever exact computation is not useful.

Control Your Time with Importance

• Solve questions whether or not the answer is obtained through straightforward logic.
• Do not require inference to solve, lick the question, and start the next: return if time allows.

Examples with Step-by-Step Solutions

Example 1: Table With Missing Information

Revenue (in INR) for a company for four months is presented in the following table:

Month Revenue

January- 120

February- 150

March- ?

April- 180

Question: What is the average revenue for the known months?

Solution: (120 + 150 + 180) ÷ 3 = 150 lakhs; the March figure is not needed.

________________________________________

Example 2: Pie Chart with Missing Sector

A pie chart provides information about the distribution of a company’s share of the market:

• A = 30%
• B = 25%
• C = 15%
• D =?

Question: What is the missing percentage?

Solution: Total = 100%. Known = 70%. Therefore, D = 30%.

Extra Practice and Resource Recommendations

To gain familiarity with some difficult sets, you can:

• Attempt to solve previous CAT papers, as they represent actual difficulty.
• Utilize practice platforms like CollegeDekho, iQuanta, Cracku, TIME, and IMS.
• Use the free DI sets you can find. Practice and exposure daily will help you become flexible and spot the patterns faster.

Missing-data DI sets in the CAT are tests of logic, not traps. If you are employing the correct CAT DILR strategy for logical reasoning, then YOU would ideally never waste your time on blanks and would focus on only solvable questions. Train yourself to find patterns instead of gaps - just this focus will improve both your accuracy and speed.

The Data Interpretation and Logical Reasoning (DILR) section of the CAT sometimes presents sets with incomplete or inconsistent data, which can be challenging. These sets are meant to test your logical reasoning, not your plug-and-chug ability. For candidates, the frustrating part is normally "missing data." This document aims to assist you in developing a reasoned approach using logical reasoning, strategic set selection, and timing techniques.

What Are DI Sets with Missing Information?

DI sets with missing information are constructed to include gaps. Gaps compel candidates to reason instead of simply computing numeric information. Gaps can be represented as follows:

• Blank cells in a table
• Partial bar or pie chart values
• Totals or subtotals are not included
• Caselet variables are not reported

The focus isn't to fill in every blank, but to extract whatever the logic of the data allows to be solved. In CAT, while it is important to consider the time it takes to arrive at a "logical" answer, even with missing data, most question types have an answer.

Key Challenges in Solving Missing-Data DI Sets

• Formula methods fail when data is missing.
• Overdoing (or over-trying) to fill in lost data accounts for wasted time and reduced efficiency.
• The logic must replace direct computation—the candidates must be able to identify constraints, patterns, or relative relationships.