Types of Statistics

Data types are crucial notions in statistics because they allow us to accurately apply statistical measurements to data and conclude certain assumptions about it.

Because you can apply certain factual metrics only for specific data kinds, having a good understanding of the various data types is critical for Exploratory Data Analysis or EDA.

Similarly, you must know the data analysis and type of data analysis you are dealing with in order to select the appropriate perceptual approach. You can think of data types as a way to organize different types of variables.

Types of data:-

If you dig deeper into statistics, you’ll find that there are only two types of data: qualitative and quantitative data. However, there is a subdivision after that, and it is divided into four sorts of data. 

Different types of data are divided into four categories:

• Nominal data
• Ordinal data
• Discrete data
• Continuous data

Nominal Data:-

Nominal data is a type of qualitative data that aids in the labeling of variables without providing a numerical value. The nominal scale is another name for nominal data. It can’t be measured or ordered. However, data can be both qualitative and quantitative at times. Letters, symbols, words, gender, and other nominal data are examples of nominal data.

The grouping method is used to analyze the nominal data. The data are categorized into categories in this manner, and the frequency or percentage of the data can then be determined. Pie charts are used to visually depict this information.

Generally nominal data are used to label variables that have no numerical value and are not ordered. As a result, changing the order of the values has no effect on the meaning.

As a result, nominal data are observed but not measured, are unsorted but non-equidistant, and lack a meaningful zero.

The only numerical actions you can do with nominal data are to say that one perception is (or isn’t) equivalent to another (equity or inequity) and to use this data to collect them.

You can’t sort nominal data because you can’t organize it.

You wouldn’t be able to do any numerical activities either, because they’re only saved for numerical data. You can determine frequencies, proportions, percentages, and central points using nominal data.

Ordinal Data:-

Ordinal data/variables are data that has a natural order to it. The difference between the data values is not determined in nominal data, which is a key aspect. This variable appears frequently in surveys, finance, economics, and questionnaires, among other places.

A bar chart is widely used to display ordinal data. Many visualization technologies are used to examine and comprehend these data. Tables can be used to represent the data, with each row representing a different category.

Ordinal data is nearly identical to nominal data, except in terms of order, as their categories can be sorted into first, second, and so on. The relative distances between adjacent categories, on the other hand, are not consistent.

Ordinal data is observed but not measured, ordered but not equidistant, and devoid of any meaningful zero. Ordinal scales are commonly used to assess happiness, satisfaction, and other emotions.

You can gather information from ordinal and nominal data by determining if they are equivalent or remarkable.

Ordinal data can be sorted by making fundamental comparisons between the categories, such as more or less than, higher or lower, and so on, because they are ordered.

Ordinal data, on the other hand, cannot be used for any numerical activity because it is numerical data.

You may calculate the same things with ordinal data that you can with nominal data, such as frequencies, proportions, percentages, and central points, but there is one more point with ordinal data: summary statistics, which are akin to Bayesian statistics.

Discrete Data:-

Only discrete values can be used with discrete data. There are only a finite number of possible values in discrete information. Those numbers can’t be subdivided in any meaningful way. Things are tallied in whole numbers here.

Continuous Data:-

Data that can be calculated is known as continuous data. It has an endless number of possible values inside a particular range that can be chosen.

Conclusion:-

The types of qualitative data or categorical data are nominal and ordinal data. The sorts of quantitative data that are also known as numerical data are 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 order ratio data.