In logic, three kinds of logical reasoning can be distinguished: deduction, induction and abduction. Given a precondition, a conclusion, and a rule that the precondition implies the conclusion, they can be explained in the following way:

1.  Deduction means determining the conclusion. It is using the rule and its precondition to make a conclusion. Example: "When it rains, the grass gets wet. It rained. Therefore, the grass is wet." Mathematicians are commonly associated with this style of reasoning.
2.  Induction means determining the rule. It is learning the rule after numerous examples of the conclusion following the precondition. Example: "The grass has been wet every time it has rained. Therefore, when it rains, the grass gets wet." Scientists are commonly associated with this style of reasoning.
3.  Abduction means determining the precondition. It is using the conclusion and the rule to support that the precondition could explain the conclusion. Example: "When it rains, the grass gets wet. The grass is wet, therefore, it may have rained." Diagnosticians and detectives are commonly associated with this style of reasoning.

Logical reasoning is a system that we use to construct an argument from observation and known facts.

We can use logical reasoning to make assumptions, through known facts. A very popular phrase that depicts logic is this: Every man is mortal, Socrates is a man, therefore Socrates is mortal.

Logic is obviously used everywhere, at work, when we are learning a new language, etc. etc.

Now something that you do not realize normally, is that your inference can be wrong even though it seems that the evidence you know concurs it to be true.

“The Ladder of Inference” -

·  Data Interpretation is the extraction of maximum information from a given set of data or information. The data can be presented either in the form of a table or a bar chart or a pie chart or a line graph or as a combination of one of these formats.

·  In CAT there is a separate section for DI & DS. In past history DI section used to consist of 5-6 sets of data followed by 5-6 easy but lengthy calculation based questions on each set. But the trend of DI section has changed a lot in last couple of years now the questions are of more logical requiring less calculation.

· In the beginning one should try to get familiarize with all data formats and solve easy questions based on single data/graph. But In real CAT questions are usually asked on combinational graph.
Before starting DI section one should be very comfortable with numbers, calculations, fractions and percentages. It helps in reducing the time required for solving the questions.

· To begin with, you should know how to interpret various data formats. Tables, line graphs, bar graphs, pie charts, three dimensional graphs, flow diagrams and cumulative frequency tables are some of the data formats that have featured in CAT over the years.

·  Data Interpretation can be defined as "the application of statistical procedures to analyze specific observed or assumed facts from a particular study". Data interpretation is something that is pretty common in education circles. They come as questions in tests to understand how much a student has understood the subject at hand. In school, college, university and higher educational levels, data interpretation is common. In various entrance exams for colleges too, data interpretation is used as a means to understand a student's grasp of the subject.

·  It is very important to understand how to interpret data in order to do well in these tests. It is especially important in case of students planning to study finance and mathematics. An interpretation question will usually contain a chart or a graph. It will also contain some data or even sets of data which the student has to analyze and come to a conclusion. When you are solving an interpretation question you will have to understand what the graph or chart means. If there are numbers involved (most probably they will be) you will have to find out what they stand for. Next, you need to draw a data set that represents the graph or chart in question. Now you have to look at the data set and come to a conclusion about what it means.

Spreadsheets

Electronic spreadsheets are often used as a tool for data interpretation, according to the University of Wisconsin-Eau Claire. These spreadsheets can sort, graph and search data.

Qualitative Data

Qualitative data can be more time-consuming to interpret than quantitative data, according to the University of Wisconsin. Researchers must actively look for contradictions, get many perspectives and collect different kinds of data to avoid bias.

Descriptive Statistics

Descriptive statistics may help with the interpretation of data. They can describe the ways data relate to each other, such as their median, deviation and variation.

Inferential Statistics

Inferential statistics let researchers make inferences about populations of data based on smaller populations. This may involve using the population's mean or standard deviation to test hypotheses.

Consideration

Absolute honesty is imperative to getting accurate results from your data interpretation, according to the University of Wisconsin.