Data in Statistics

Statistics is a branch of mathematics that deals with the gathering, organising, analysing, interpreting, and presenting of data. All aspects of data, including survey and experiment design and data collection planning, are covered by statistics.

Data in Statistics

It’s a set that represents a collection of different values of that integer. It’s a collection of data and figures that will be used for a certain reason, such as a survey or research project. Data that has been logically organised is defined as information. The source of the data (original data vs. secondary data) is equally important.

Types of Data

Data is divided into two categories: qualitative and quantitative. You can use them once you’ve learned how to distinguish between them.

• Qualitative or categorical data:

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

They represent particular characteristics or characteristics. They display visual descriptions that cannot be estimated or computed. For example, statistics on intelligence, honesty, wisdom, cleanliness, and originality collected from a random sample of your class’s students would be considered qualitative. They have a more inquisitive than determined personality.

Qualitative data is of two kinds:

• Nominal data:

Nominal data refers to variables that have no numerical value or order. Because one colour cannot be compared to another, hair colour might be considered fictional data.

With nominal data, we can’t do anything numerically, and we can’t arrange the data in any sequence. The word “nominal” comes from the Latin word “nomen,” which literally means “name.” This data has no obvious hierarchy, and the values are dispersed throughout numerous categories.

• Ordinal data:

Natural ordering is present in ordinal data, which means that numbers appear in some order based on their scale location. These statistics are important for observing things like customer satisfaction and pleasure, but we can’t do anything with them mathematically.

Ordinal data is a type of qualitative data in which the values are arranged in a hierarchical order. These data can be categorised as “in-between” qualitative and quantitative information. Ordinal data is only useful for visualising sequences and not for statistical analysis. Ordinal data, as opposed to nominal data, have some sort of order that nominal data does not.

• Quantitative data:

Quantitative data may be tallied and involves statistical data analysis since it is expressed in numerical numbers. Numerical data is another name for this sort of information. “How much,” “how many,” and “how frequently” are some of the inquiries it answers. Quantitative data includes things like a phone’s pricing, a computer’s RAM, a person’s height and weight, and so on. Rather than being observed, they can be measured. They can be represented quantitatively and used in calculations. Data on how many students in your class participate in various sports, for example, can be used to estimate how many of the total pupils participate in each sport. Because this information is numerical, it can be classified as quantitative.

Quantitative data is of two kinds:

• Discrete data:

Discrete refers to something that is separate or distinct. Discrete data consists of values that fall within the integer or whole number category. Discrete data includes things like the total number of students in a class. There is no way to convert these integers to decimal or fractional values.

Discrete data may be counted and given finite values, but not divided. A bar graph, a number line, or a frequency table are the most common ways to represent this data.

• Continuous data: 

Fractional numbers are used to represent continuous data. For example, it may be the Android phone’s version, a person’s height, or the length of an object. Continuous data is information that can be broken down into smaller pieces. Any value within a range can be assigned to the continuous variable.

Conclusion

• Nominal and ordinal data are two types of qualitative or categorical data

• Interval data and ratio data are examples of quantitative data that are also known as numerical data

• Nominal data are unsorted, non-equidistant, and lack a meaningful zero, and they are observed rather than measured

• Ordinal data is observed rather than measured, and it is organised in a non-equidistant fashion with no meaningful zero

• There is no meaningful zero in interval data, despite the fact that it is measured and organised with equidistant parts

• Ratio data is also measured and organised using equidistant pieces and a meaningful zero