Data Science and Artificial Intelligence

Inferential Statistics – Definition and Types

If you have or are just starting to delve deeper into statistics, you now know that statistics can be broadly classified into two types – descriptive and inferential statistics.

There are many differences between descriptive and inferential statistics. Inferential statistics is extremely useful in data analytics, and any capable data scientist must have an idea of what it is in order to understand and solve many real-world problems fully.

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So what is inferential statistics, and what are the types of inferential statistics? Let us find out.

What is Inferential Statistics?

Inferential statistics is generally used when the user needs to make a conclusion about the whole population at hand, and this is done using the various types of tests available. It is a technique which is used to understand trends and draw the required conclusions about a large population by taking and analyzing a sample from it. Descriptive statistics, on the other hand, is only about the smaller sized data set at hand – it usually does not involve large populations. Using variables and the relationships between them from the sample, we will be able to make generalizations and predict other relationships within the whole population, regardless of how large it is.

Types of Inferential Statistics Tests

There are many tests in this field, of which some of the most important are mentioned below.

1.      Linear Regression Analysis

In this test, a linear algorithm is used to understand the relationship between two variables from the data set. One of those variables is the dependent variable, while there can be one or more independent variables used. In simpler terms, we try to predict the value of the dependent variable based on the available values of the independent variables. This is usually represented by using a scatter plot, although we can also use other types of graphs too.

2.      Analysis of Variance

This is another statistical method which is extremely popular in data science. It is used to test and analyse the differences between two or more means from the data set. The significant differences between the means are obtained, using this test.

3.      Analysis of Co-variance

This is only a development on the Analysis of Variance method and involves the inclusion of a continuous co-variance in the calculations. A co-variate is an independent variable which is continuous, and are used as regression variables. This method is used extensively in statistical modelling, in order to study the differences present between the average values of dependent variables.

4.      Statistical Significance (T-Test)

A relatively simple test in inferential statistics, this is used to compare the means of two groups and understand if they are different from each other. The order of difference, or how significant the differences are can be obtained from this.

5.      Correlation Analysis

Another extremely useful test, this is used to understand the extent to which two variables are dependent on each other. The strength of any relationship, if they exist, between the two variables can be obtained from this. You will be able to understand whether the variables have a strong correlation or a weak one. The correlation can also be negative or positive, depending upon the variables. A negative correlation means that the value of one variable decreases while the value of the other increases and positive correlation means that the value both variables decrease or increase simultaneously.

Now that you know what inferential statistics are and how important these tests are, you can start your journey to become a capable data scientists using the many courses that Acadgild has to offer!


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