Consider a situation in which you want to know the most liked subject of the students of your class. You started with collecting the required data and collected the data from all the students. But, can you say that the data collected will be useful to you? No. Unless and until you organize the data, you cannot say that the data will help you. The unorganized data collected is called raw data. Now, the job is to organize the data and then interpret it accordingly. One of the ways to organize data is using tally marks.
Grouping and organizing data: Tally Marks
Let us consider an example to understand the concept of tally marks, suppose you have the data of most liked TV channels among the Channels A, B and C from 10 people. The raw data you have is in the form:
Channel A, Channel B, Channel A, Channel C, Channel A, Channel B, Channel B, Channel A, Channel A, Channel A. This data is unorganized and to interpret it, we can represent using tally marks as follows:
Tally Chart
In the above table, | means 1.
The number of tallies before the channel represents the number of people liking it. This is called frequency. In the above case, frequency of channel A is 6, frequency of channel B is 3 and frequency of channel C is 1.
After the collection and organization of data, sometimes it is necessary to group data as well. Data can be big or small, based on requirement of the user. Suppose you need to know the average marks of a school having 1500 students. Data collection and organization will be tiring, as the numbers are huge. In such cases, when the data is large, it becomes necessary to group the data as well. Say the collected marks were:
10, 20, 60, 40, 32, 67, 45, 76, 210, 67, 45, 90, 87, 56, 34, 56, 78, 4 3, 65, 32, 78, 09, 3, 67, 89, 12, 43, 45, 66, 44, 23, 11, 87, 5, 37, 63, 48, 95, 44, 22, 78, 43, and so on for 1500 students.
It will be really easy if we can categorize the data in fixed intervals. Say, we divided the marks in intervals of 10, i.e. 0-10, 10-20, 20-30, 30-40 and so on till 90-100. Then it will be easy to understand and interpret the data.
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