4 Types of Data in Statistics: Nominal, Ordinal, Discrete & Continuous

Experimenting with raw or structured data is central to data science. Data is the gasoline that may propel a company in the correct direction, or at the very least provide actionable information that can be used to strategize current campaigns, plan the introduction of new goods, or test new ideas. Data is the common driving force behind all of these activities. We're approaching the digital era, which means we'll be producing a lot of data. For example, a company like Flipkart generates around 2TB of data per day.

When data is so crucial in our lives, it must be correctly stored and processed without error.

Since data is important in everyday life, it should be maintained and handled accurately and without mistakes. When dealing with datasets, a certain kind of data is crucial in identifying which processing approach will get the best possible results or which sort of statistical analysis must be utilised to attain the best results. Let's look for some of the most common data kinds.

Qualitative Data Type

The object under investigation is described using a finite set of discrete classifications in qualitative or categorical data. It signifies that this type of information can't be simply counted or assessed using numbers, hence it's divided into categories. An excellent example of this data type is a person's gender (male, female, or other). These are typically derived from audio, image, or text sources. Another example is a smartphone brand that displays information such as the current rating, the phone's color, the phone's category, and so on. All of this data can be classified as qualitative data. This is divided into two subcategories:

• Nominal

These are the values that don't have a natural order to them. Let's look at some examples to help you understand. Because we can't compare one color to another, the color of a smartphone can be regarded as a notional data type. It's impossible to say that 'Red' is superior to 'Blue.' Another area where we can't distinguish between male and female is a person's gender. Nominal data types apply to all smartphone categories, whether they are midrange, budget, or premium.

• Ordinal

While keeping their class of values, certain types of values have a natural ordering. If we analyze a clothing brand's size, we can simply categorize them into small, medium, and large categories based on their name tag. The grading method used to score candidates on an exam can also be thought of as an ordinal data type, with A+ being significantly better than B. These classifications assist us in determining which encoding approach is appropriate for which sort of data.

Data encoding is crucial for qualitative data because machine learning models can't handle these values directly and must be translated to numerical types because the methods are mathematical. One-hot encoding, which is comparable to binary coding since there are fewer categories, can be used for nominal data types, while label encoding, which is a type of integer encoding, can be used for ordinal data types where there are no comparisons among the categories.

Quantitative Data Type

This data type attempts to quantify things by taking into account numerical values that make them countable. Quantitative data kinds include the price of a smartphone, the discount offered, the number of product ratings, the frequency of a smartphone's CPU, and the ram of that particular phone.

The important thing to remember is that a feature can have an endless number of values. For example, the cost of a smartphone can range from x dollars to any dollar figure, and it can be further broken down into fractional dollars. The following are the two subcategories that best characterize them:

• Discrete

This category is made up of numerical values that are either integers or whole numbers. Discrete data types include the number of speakers in a phone, cameras, processing cores, and the number of sims supported, to name a few.

• Continuous

Continuous values are applied to fractional numbers. These can include things like the processors' working frequency, the phone's Android version, wifi frequency, core temperature, and so on.

Is it possible for Ordinal and Discrete types to overlap?

If you pay attention to this, you can number the ordinal classes, and then decide whether they should be labeled discrete or ordinal. The reality is that it is still commonplace. This is because, even if the numbering is done correctly, it does not express the actual distances between the classes. Consider the grading system for a test, for example.

The grades can be A, B, C, D, or E, and if we counted them from the beginning, they would be 1,2,3,4,5. The distance between E and D grades is now the same as the distance between D and C grades, which is not accurate because we all know that C grade is still acceptable when compared to E grade, but the mid difference deems them equal.

Final Words

In this post, we looked at how the data we generate can turn the tables on us, and how different types of data are organized based on their use. We also looked at how ordinal data types and discrete data types might overlap. The various sorts of tests that may be applied to certain data types and other tests that employ all types of data were also reviewed, as well as which type of plot is appropriate for which category of data.