The statistical analyses in Inferential Statistics enable you try to make inferences about population mean from the sample data when you have no idea of population mean. 

However, sometimes you have some starting assumption about the population mean and you want to confirm those assumptions using the sample data. It is here that hypothesis testing comes into the picture. We will cover the basic concepts of hypothesis testing, which are as follows:

• Types of hypotheses
• Types of tests
• Decision criteria
• Critical value method of hypothesis testing

Here we will discuss the concepts of Hypothesis Testing from the theory perspective

• Exploratory data analysis: Exploring data for insights and patterns
• Inferential statistics: Making inferences about the population using the sample data.

Now, these methods help you formulate a basic idea or conclusion about the population. Such assumptions are called “hypotheses”.

Let’s understand the basic difference between inferential statistics and hypothesis testing.

Inferential statistics is used to find some population parameter (mostly population mean) when you have no initial number to start with. So, you start with the sampling activity and find out the sample mean. Then, you estimate the population mean from the sample mean using the confidence interval.

Hypothesis testing is used to confirm your conclusion (or hypothesis) about the population parameter (which you know from EDA(Exploratory data analysis ) or your intuition). Through hypothesis testing, you can determine whether there is enough evidence to conclude if the hypothesis about the population parameter is true or not.

Hypothesis Testing starts with the formulation of these two hypotheses:

• Null hypothesis (H₀): The status quo
• Alternate hypothesis (H₁): The challenge to the status quo

Failure to reject null hypothesis (Not Equal) Accept null hypothesis

One can only fail to reject the null hypothesis, you can never accept the null hypothesis.

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