More critical than organising data is understanding the data collected. Let’s say you want to find out how well your friend does on a survey. Then you go to your instructor and ask to see his letter, but he tells you he can’t offer you the response sheet and can give you the grades. As a result, the instructor informs you of the subject’s grades, which you record and give to your mate. When on the way, you find that your buddy has earned the same grades as you. Is this a coincidence? What occurred here was that the instructor told you your own grades, thinking you were there to double-check them. As a result, the whole data set you just gathered is incorrect. And if you have a chart of marks with you, that is not the information you were looking for. When dealing with evidence, it’s essential to understand that the same data is being gathered in the first place.

It was previously stated that data must be interpreted in an ordered and contextual way to be useful to us. As a result, the significance of data organisation cannot be overstated.

You’ve already come through the classic yellow pages. These massive books included the name and phone number of any person who lived in a given area. And if it was a huge metropolis, thousands of people’s names and phone numbers were in that book. So, if you’re visiting a city you’ve never been to before and want to see a buddy you know lives there, all you have to do is look at the yellow pages for his name and phone number. Isn’t it a lifesaver if you get lost?

Don’t you need a lot of numbers from a lot of people to create such a book? The receiving of numbers is the simple part. If you send a group of people around town asking for people’s phone numbers, you’ll quickly amass a large amount of information. For this data to be usable, it must be organised in a way that makes finding easier. The names in the yellow pages are alphabetically organised, so if you know a word, you can look it up much as you might in a dictionary.

Tally markers should be used where there are many observations to reduce the number of mistakes and render tabulation simpler. Three columns are drawn using the tally marks system. Write runs in the first section, tally points in the second column, and the number of teams in the third column. Tally marks are held in five-count groups. To make a group of five, trace the fifth tally line diagonally over the first four.

The usage of intuitive charts to easily interpret and simplify data sets is referred to as graphical representation. Data is ingested into data software’s graphical image and described by a series of markers – such as bars on a bar chart, lines on a line map, or slices on a pie chart – from which consumers can obtain more information than by numerical analysis alone.

Representational diagrams may help forecast and make smarter evidence-driven judgments by illustrating general behaviour and highlighting phenomena, anomalies, and interactions between data points that would otherwise go unnoticed. The form of evidence investigated would determine the kinds of representational graphics used.

The most common and well-known indicator of central tendency is the mean (or average).

The median is the score that falls in the centre of a series of data grouped according to magnitude.

Mean = sum of all observations/no. of observations

Median = {(n+1)/2}th term

In this chapter, we learned about the basics of data handling. We can further use this knowledge to assess the collection of data to examine a hypothesis.

1. What are the four different types of data?
   There are four types of data measurement measures in statistics: nominal, ordinal, interval, and ratio.
2. What does the data handling process entail?
   During and after completing a research project, data handling is the process of making sure that research data is stored, archived, or disposed of safely and securely.
3. What is the difference between the two types of data?
   Quantitative and qualitative data are the two main categories of data, and both are equally relevant.
4. What does mean in data handling indicate?
   The sum of the data divided by the total number of data is the mean of a set of numbers, known as the average.
5. In terms of data handling, what is the median?
   Sort the data points in ascending order from smallest to highest. The median is the middle data point in the list if the number of data points is odd. The median is the average of the two middle data points in the list if the number of data points is even.