Data Interpretation

Data interpretation is the process through which inferences are drawn about the data available for analysis. In other words, the process of drawing inferences and conclusions through the interpretation of data is what data interpretation is all about.

In the case of questions related to data interpretation, data will be given in the form of any of the below mentioned tools followed by questions pertaining to the same. These questions are intended to test the ability of the student to interpret the information presented and to select the appropriate data for answering a question.

Tips to solve data interpretation questions

  • Read the data very carefully. Even the minutest word must not be overlooked since many a times even a single word or phrase could become critical.
  • The easiest way to solve a data analysis question is to weed out the incorrect options from the given options. While doing so, stop the weeding out process at once you get the correct option. Do not try to check the next option if you get one correct answer. Always remember that time is very important.
  • If there are more than one graphs or charts or tables, understand the relationship between them clearly before you proceed to solve the questions asked.
  • Answer only the questions asked. Do not answer or calculate things which have not been asked for.
  • The given data may be insufficient for interpreting some answer options. Avoid those options at once.

For the data interpretation questions, data will be represented in any of the data presentation tools like tables, pie-charts, bar graphs etc. Therefore, we can have an overview of the different types of questions using these data presentation tools.

1      Table

A table is a display of data arranged into rows and columns. Almost any quantitative information can be organized into a table. A table consists of horizontal rows and vertical columns. The heading for each row and column helps the reader understand the data and the units used for the same.

2      Bar chart

A bar chart or bar graph is a chart with rectangular bars with lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally. Bar charts are used for plotting discrete (or discontinuous) data; that is data which has discrete value and is not continuous. Bar graphs consist of an axis and a series of labelled horizontal or vertical bars that show different values for each bar. The numbers along a side of the bar graph are called the scale. The important point to note about bar graphs is their bar length or height—the greater their length or height, the greater their value.

3      Line graph

A line graph is a useful data presentation tool for showing a long series of data. Line graphs are also useful for comparing several different series of data in the same graph. Line graphs display data in two dimensions. We call the dimensions the x-axis and the y-axis.

By convention the dependent variable or y variable is on the vertical axis and the independent or x variable is on the horizontal axis. When reading a line graph, you will notice that rise and falls in the line show how one variable is affected by the other.

4      Histograms

A histogram is a graphical display of data using bars of different heights. It shows a result of continuous data, such as: weight, height etc. It is used to summarize discrete or continuous data that are measured on an interval scale. A histogram divides up the range of possible values in a data set into classes or groups.

5      Pie charts

A pie chart is a circular chart divided into sectors. In a pie chart, the arc length of each sector is proportional to the quantity it represents. When a pie chart is formed from a data, it breaks up a whole into its parts. The share of each part in a pie chart is proportionate to its share of the whole data.

“For detailed theory, refer the book “CSIR-NET General Aptitude – A New Outlook”

Leave a Reply

Your e-mail address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.