- Mastering Python for Data Science
- Samir Madhavan
- 229字
- 2021-07-16 20:14:18
Chapter 2. Inferential Statistics
Before getting understanding the inferential statistics, let's look at what descriptive statistics is about.
Descriptive statistics is a term given to data analysis that summarizes data in a meaningful way such that patterns emerge from it. It is a simple way to describe data, but it does not help us to reach a conclusion on the hypothesis that we have made. Let's say you have collected the height of 1,000 people living in Hong Kong. The mean of their height would be descriptive statistics, but their mean height does not indicate that it's the average height of whole of Hong Kong. Here, inferential statistics will help us in determining what the average height of whole of Hong Kong would be, which is described in depth in this chapter.
Inferential statistics is all about describing the larger picture of the analysis with a limited set of data and deriving conclusions from it.
In this chapter, we will cover the following topics:
- The different kinds of distributions
- Different statistical tests that can be utilized to test a hypothesis
- How to make inferences about the population of a sample from the data given
- Different kinds of errors that can occur during hypothesis testing
- Defining the confidence interval at which the population mean lies
- The significance of p-value and how it can be utilized to interpret results
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