Data handling begins with asking the right questions to collect information systematically. When we want to find out preferences (like the 'most liked subject'), the question must be clear and specific.
Consider the questions asked by Rohan and Anjali to find the most liked subject in their class:
To record responses efficiently, we can use short codes or abbreviations for each category:
Example of recorded raw data for $45$ children:
T, A, P.E., A, M, P.E., L, L, P.E., A, T, M, A, P.E., P.E., T, P.E., M, L, P.E., P.E., A, M, A, L, P.E., M, T, A, T, M, M, P.E., A, L, M, L, P.E., A, T, L, M, M, T, L
Raw data can be difficult to read and interpret. Organizing it into tables makes it much easier to understand and analyze.
By counting the occurrences of each abbreviation in our recorded data, we can fill out a structured table to find the number of children who prefer each subject:
There are two main ways to display this information:
An organized table is much easier to understand because it allows us to quickly identify the most common and least common favorites without counting the entire list manually.
A Pictograph is a way of representing data using pictures or symbols to represent quantities.
Rohan and Anjali recorded the ice golas eaten by different children during lunch break to find the most popular color:
Both methods help us easily visualize which color gola is eaten the most and which is eaten the least.
Anjali and Rohan recorded the sales of Fruit Chaats and Sandwiches over three days using pictographs:
By counting the symbols, we can answer questions such as which day had the highest sales and which item was more popular.
Sometimes we need to organize data based on more than one category at the same time (e.g., gender and game preference).
To find out how many boys and girls play chess, cricket, both, or neither, we can organize the data into a two-way table with columns for both girls and boys across different game categories.