Discuss the need for Statistical Hypothesis Testing with the help of an example. Explain types of Errors in Hypothesis Testing

Statistical Hypothesis Testing is a critical component in data science and big data analytics. Its primary purpose is to make inferences about populations based on sample data. Let's delve into the need for hypothesis testing with an example and then explore the types of errors associated with it.

### Need for Statistical Hypothesis Testing:

Consider a scenario in which a pharmaceutical company develops a new drug intended to reduce blood pressure in patients. They run an experiment to assess its efficacy compared to the existing drug on the market.

Scenario:

- Null Hypothesis (H0): The new drug has no effect on reducing blood pressure compared to the existing drug.
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