# STATISTICS

- [Probability Basics](https://book.thedatascienceinterviewproject.com/statistics/probability-basics.md): Probability theory is the mathematical foundation of statistical inference, which is indispensable for analyzing data affected by chance, and thus essential for data scientists.
- [Probability Distribution](https://book.thedatascienceinterviewproject.com/statistics/probability-distribution.md): Knowing the distribution of data helps us better model the world around us. It helps us to determine the likeliness of various outcomes or make an estimate of the variability of an occurrence.
- [Central Limit Theorem](https://book.thedatascienceinterviewproject.com/statistics/central-limit-theorem.md): The theorem gives us the ability to quantify the likelihood that our sample will deviate from the population without having to take any new sample to compare it with.
- [Bayesian vs Frequentist Reasoning](https://book.thedatascienceinterviewproject.com/statistics/bayesian-vs-frequentist-reasoning.md)
- [Hypothesis Testing](https://book.thedatascienceinterviewproject.com/statistics/hypothesis-testing.md): Hypothesis testing is the process used to evaluate the strength of evidence from the sample and provides a framework for making determinations related to the population
- [A/B test](https://book.thedatascienceinterviewproject.com/statistics/a-b-test.md)


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