Types of Data in Statistics

Statistics can be defined as the branch of mathematics that deals with the gathering, organising, analysing, interpreting, and presenting of data. statistics is all about the leaning with all aspects of data, including data collection planning in terms of survey and experiment design.

Types of Data

1. Qualitative or Categorical Data

Data that cannot be quantified or tallied in the form of numbers is referred to as qualitative or categorical data. These kinds of data are organised by category rather than by number. Categorical data is the name given to it because of this. Audio, pictures, symbols, and text are all examples of data. A person’s gender, whether male, female, or other, is qualitative data.

People’s perceptions are revealed via qualitative data. This information aids market researchers in gaining a better understanding of their clients’ preferences, allowing them to tailor their ideas and plans appropriately.

The following are some further examples of qualitative data: what language do you speak?, favourite vacation location, a point of view on something (agree, disagree, or neutral), and colours etc.

Qualitative data is of two kinds:

• Nominal data

Nominal data is used to designate variables that have no numerical value or order. Because one colour cannot be compared to another, hair colour might be considered notional data.

“nominal” is derived from the latin word “nomen,” which means “name.” we can’t execute any numerical activities with nominal data, and we can’t arrange the data in any order. These data have no discernible order, and their values are dispersed throughout many categories.

Nominal data examples are hair colour is (blonde, red, brown, black, etc.), relationship status (single, widowed, married), nationality is a term used to describe a (indian, german, american), gender is a factor (male, female, others), color of the eyes (black, brown, etc.)

• Ordinal data

Ordinal data contain natural ordering, which means that numbers appear in some form of order based on their scale location. These data are useful for observational purposes such as customer satisfaction and pleasure, but we are unable to do any mathematical operations on them.

Ordinal data is a kind of qualitative data in which the values have a relative position. These data may be thought of as being “in-between” qualitative and quantitative data. Ordinal data can only be used to display sequences and is not suitable for statistical analysis. Ordinal data, as contrast to nominal data, contain some type of order that nominal data lacks.

Ordinal data examples: when organisations ask for feedback, experience, or satisfaction on a scale of one to ten, they are asking for a scale of one to ten, in the test, you will get letter grades (a, b, c, d, etc.), in a competition, individuals are ranked (first, second, third, etc.)

2. Quantitative Data

Quantitative data is countable and incorporates statistical data analysis since it may be stated in numerical quantities. Numerical data is another name for this kind of information. It provides answers to inquiries such as “how much,” “how many,” and “how often.” quantitative data includes things like a phone’s pricing, a computer’s ram, a person’s height or weight, and so on.

Quantitative data examples: a person’s or an object’s height or weight, temperature in the room, marks and scores (ex: 59, 80, 60, etc.), time

Quantitative data is of two kinds:

• Discrete data

The phrase discrete refers to anything that is unique or separate. The values that come within the category of integers or whole numbers make up the discrete data. Discrete data includes things like the total number of students in a class. There is no way to convert these numbers into decimal or fractional values.

Discrete data may be counted and have finite values; however, they cannot be subdivided. A bar graph, a number line, or a frequency table are the most common ways to show this information.

Discrete data examples are the total number of students in a class, the price of a mobile phone, employees in a company’s number, the total number of competitors who took part in a competition, the number of days in a week etc.

• Continuous data: 

Fractional numbers are used to represent continuous data. It might be the android phone’s version, a person’s height, the length of an item, and so on. Continuous data is information that can be broken down into smaller chunks. Any value within a range may be assigned to the continuous variable.

Continuous data examples are a person’s height, a vehicle’s top speed, “time taken” to complete the task, frequency of wi-fi, and price of a market share etc.

Conclusion

• The kinds of qualitative data or categorical data are nominal and ordinal data
• The sorts of quantitative data that are also known as numerical data include interval data and ratio data
• Nominal data are observed rather than measured, and they are unsorted, non-equidistant, and lack a meaningful zero
• Ordinal data isn’t measured, but rather observed, and it’s arranged in a non-equidistant manner with no meaningful zero
• Despite the fact that interval data is measured and arranged with equidistant pieces, there is no meaningful zero
• Equidistant items and a meaningful zero are also used to measure and organise ratio data