Types of Statistics

Types of statistics refer to the various branches of statistics that are used to describe, summarize and analyze data. Many different operations can be performed on data, thus, it is necessary to categorize them on the basis of certain features. These types of statistics help to collect data in an organized form and then draw inferences from it.

There are broadly two types of statistics - descriptive statistics and inferential statistics. In this article, we will learn more about these types of statistics, the different types of data used, their corresponding variables as well as see certain associated examples.

The various types of statistics are required for the collection, description, organization, analysis, and interpretation of data. These types of statistics help to describe certain attributes related to the data as well as estimate the parameters of the population by analyzing samples. Given below are the two types of statistics.

Descriptive Statistics

One of the types of statistics is descriptive statistics. It is used to quantitatively describe the attributes of the known data and provides summaries of either the sample or the population. Graphs, charts, and tables can be used to represent descriptive statistics. The measures of descriptive statistics are given as follows:

• Measures of central tendency: These measures are used to describe data with respect to a single central point. Mean, median, and mode are three types of central tendency.
• Measures of dispersion: These measures are used to describe the variability of data. In other words, it is used to quantify the spread of a distribution about a central value. Range, variance, standard deviation, mean deviation, quartile deviation, and coefficients of dispersion are the types that fall under this category.

Inferential Statistics

Inferential statistics are used when generalizations about the population need to be made and conclusions need to be drawn based on the available samples. Inferential statistics uses various types of statistical tests and sampling techniques in order to make inferences about the population data. The measures of inferential statistics are given below:

• Hypothesis testing - It is used to test some assumptions and make inferences about the population parameters by using an estimate of the sample. There are many types of statistical tests used for this purpose. Some of them are the z test, t test, f test, and ANOVA test.
• Regression Analysis - This type of analysis is used when the effect of change in one variable causing a change in another variable needs to be evaluated and quantified. Simple linear, multiple linear, nominal, logistic, and ordinal regression are the types of regression analysis.

Before the various tools used in the different types of statistics can be deployed, it is necessary to also understand the types of data. The upcoming section will elaborate on the types of data and variables used in statistics.

There are two types of data in statistics, namely, qualitative data and quantitative data. In data analysis, it is necessary to apply the correct testing technique which can only be done by sorting the data into various types. Given below are the different types of data in statistics.

Qualitative Data or Categorical Data

• Nominal data - This type of data can be divided into two mutually exclusive groups that do not overlap. Labels and tags are used to categorize such data. Nominal data does not have any intrinsic ordering and does not possess any numeric properly. Examples of nominal data are gender, eye color, etc.
• Ordinal data - Similar to nominal data, arithmetic, and logical operations cannot be performed on ordinal data as it does not possess any numerical property. However, such a type of data can be intrinsically ordered. For example, rating a restaurant experience on a scale of 1 - 5.

Quantitative Data

• Discrete data - This is one of the types of data that can only involve the use of integers and cannot be divided into smaller or finer parts. Mathematical operations can be performed on discrete data. For example, days of a month, number of teachers in a school, etc.
• Continuous data - Such a type of data can be divided into finer levels and can take on any numeric value. There are two types of continuous data, namely, interval and ratio data. Interval data can be negative and does not have a meaningful zero. On the other hand, ratio data can never be negative and has a meaningful zero. Calculations for continuous data are performed using descriptive statistics.

Just as there are two types of data, similarly, there are two types of variables in statistics. These variables are used to represent the corresponding data. In both types of statistics, it is necessary to choose the right kind of variable so as to administer the appropriate statistical test. Given below are the different types of variables in statistics:

Qualitative Variables

• Binary or dichotomous variables - Such a type of variable can represent nominal data with only two levels of outcomes and does not possess any intrinsic ordering. For example, passing or failing in an examination.
• Nominal variables - Nominal variables are used to represent data that does not have any rank and cannot be ordered intrinsically. In other words, it is used to represent nominal data. For example, breeds of dogs.
• Ordinal variables - This type of variable is used to represent ordinal data wherein the groups can be ranked in a specific order. An example is the finishing rank of people in a race.

Quantitative Variables

• Discrete variables - Discrete variables represent the counts of unique items or values. Such variables are also known as integer variables. The different types of flowers in a garden can be represented using discrete variables.
• Continuous variables - These variables, also known as ratio variables, are used to denote continuous data. It is used to represent non-finite and continuous values. For example, the volume of a sphere.

Related Articles:

• Data Collection Methods
• Summary statistics
• How to Find Median

Important Notes on Types of Statistics

• Types of statistics refer to the branches of statistics used to collect, describe, and draw conclusions from data.
• There are two types of statistics - descriptive and inferential statistics.
• In statistics, there are two types of data, namely, qualitative and quantitative data.
• As there are two types of data thus, there are two types of variables in statistics (qualitative and quantitative).

What Do You Mean By Types of Statistics?

Types of statistics refer to the various branches of statistics that fall under applied mathematics. These branches are used to describe data and draw inferences about a large population using analytical tools.

What are the Two Types of Statistics?

The two types of statistics are descriptive statistics and inferential statistics. Descriptive statistics describe data and inferential statistics draw inferences from samples regarding the population data.

What are the Measures of the Types of Statistics?

The measures of the 2 types of statistics are given as follows:

• Descriptive statistics - measures of central tendency and measures of dispersion.
• Inferential statistics - hypothesis testing and regression analysis.

What Types of Statistics are Used to Describe Data?

The type of statistics used to describe data is descriptive statistics. It uses various measures such as mean, variance, mode, etc., to quantitatively describe the attributes of the sample or the population.

What Types of Statistics are Used to Analyze Data?

The type of statistics used to analyze data is inferential statistics. It is used to draw conclusions and make generalizations about the population data by analyzing the sample data.

How Many Types of Data in Statistics are Used?

There are 2 types of data in statistics as given below:

• Qualitative data or categorical data - nominal and ordinal data.
• Quantitative data - discrete and continuous data.

How Many Types of Variables in Statistics are Used?

There are two main types of variables in statistics given as follows:

• Qualitative variable - binary, nominal and ordinal variable.
• Quantitative variable - discrete and continuous variable.