Understanding Hypothesis Testing

In the last two modules, you learned about the following topics:

  • 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”. But how do you really confirm these conclusions or hypotheses? Let’s see.

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 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.

Both these modules have a few similar concepts, so don’t confuse terminology used in hypothesis testing with inferential statistics.

Lets get started by understanding the basics of hypothesis testing.

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

Now, having got a brief idea about what hypothesis testing is, in the next page, we will look at its different aspects in detail, starting with the formulation of the null and alternate hypotheses.

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