https://github.com/ahammadmejbah/statistics-interview-questions

Statistics is a branch of mathematics that involves collecting, analyzing, interpreting, and presenting data. It provides tools and techniques to summarize information, make inferences, and support decision-making in various fields, from science and business to social research.
https://github.com/ahammadmejbah/statistics-interview-questions

data-science deep-learning interview machine-learning python statistics

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Statistics is a branch of mathematics that involves collecting, analyzing, interpreting, and presenting data. It provides tools and techniques to summarize information, make inferences, and support decision-making in various fields, from science and business to social research.

• Host: GitHub
• URL: https://github.com/ahammadmejbah/statistics-interview-questions
• Owner: ahammadmejbah
• License: gpl-3.0
• Created: 2023-10-07T09:47:04.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-10-07T10:07:47.000Z (about 1 year ago)
• Last Synced: 2024-08-28T20:40:55.485Z (3 months ago)
• Topics: data-science, deep-learning, interview, machine-learning, python, statistics
• Homepage: https://blogs.ahammedmejbah.com/
• Size: 18.6 KB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# **Question: 01** 

### **What is the Central Limit Theorem, and why is it important in statistics?**

**Answer:** The Central Limit Theorem (CLT) is a fundamental concept in statistics. It states that, regardless of the shape of the original population distribution, the sampling distribution of the sample means will approximate a normal distribution as the sample size increases. In other words, if you were to take multiple random samples from a population and calculate the means of those samples, the distribution of those sample means will be approximately normal, even if the original population is not normally distributed.

**Explanation:** The Central Limit Theorem is important in statistics for several reasons:

1. **Inference:** It forms the basis for many statistical inference techniques, such as hypothesis testing and confidence intervals. These techniques rely on the assumption of normality, and the CLT allows us to use them in a wide range of practical situations.

2. **Simplification:** Normal distributions are well-understood and have many mathematical properties that make statistical analysis easier. The CLT simplifies the analysis of data because we can often treat the sample means as normally distributed, even when dealing with non-normally distributed data.

3. **Real-world applicability:** Many real-world phenomena can be approximated by a normal distribution due to the CLT. This makes it a valuable tool in fields like quality control, finance, and social sciences, where data often exhibits some level of variability.

In summary, the Central Limit Theorem is a cornerstone of statistical theory because it allows us to make inferences and draw conclusions about populations based on sample data, even when we don't know the exact population distribution, as long as we have a sufficiently large sample size.