Types of Statistics

So far, we have studied what Statistics is and what types of problems it can solve. Now we will study about its types. It is divided into two parts:

1. Descriptive Statistics
2. Inferential Statistics
   

1) Descriptive Statistics:

It is where we describe our data.
We use relevant charts to visualize our data so that some meaningful information can be extracted out of it.
We should remember that “Data” in itself has no value. We need to apply some transformations or do some visualizations to extract “Information” out of it. Therefore, descriptive statistics helps us in unboxing the hidden secret.

Below diagram shows all techniques that falls under descriptive statistics.

2) Inferential Statistics:

It helps us in concluding about a population from a sample.

We take one sample, analyze it and at the end we draw some conclusions from this sample which is applicable for a population.

For. e.g. What is the average height of people living on earth?

First, we will consider one sample set of people (this sample can have 10,000 people or 1,00,000 people or any other number), and we apply Inferential Statistical techniques to understand this sample and come up with the average height for whole population

Below diagram shows all techniques that fall under inferential statistics.

Difference between Descriptive and Inferential Statistics?

Descriptive Statistics is limited to sample set only. It helps in visualizing and summarizing the current sample set.

For e.g. Take one sample of 1,000 apples, find the mean of their weights.

Result: The average weight of the sample taken (1,000 apples) is 120 grams

Descriptive Statistics does not generalize anything about the apples other than the current sample set taken.

Whereas, Inferential Statistics helps in analyzing the current sample set and helps in predicting or concluding about the whole population.

Note: While finding Inferential Statistics, it is very important that your sample should be a representative of the whole population, otherwise the conclusions/inferences will not be effective enough. We will learn all the sampling techniques also which help in taking a good sample set.