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Measure theory is a branch of mathematics that provides a systematic way to assign sizes or volumes to sets, with the goal of generalizing concepts such as length, area, and volume. In the study of probability and statistics, measure theory plays a crucial role in providing a rigorous foundation for defining probabilities and analyzing random variables.

By using measure theory, probabilities can be defined as measures on a probability space, and random variables can be defined as measurable functions between different measurable spaces. This allows statisticians to work with complex probability distributions and perform operations such as integration and transformation with a high level of mathematical rigor.

Overall, measure theory provides the mathematical framework necessary to study complex probability models, develop statistical methods, and ensure the validity of statistical inference procedures.
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Mathematical concepts such as measure theory are fundamental in the study of probability and statistics because they provide a rigorous framework for defining probabilities and properties of random variables. Measure theory allows us to formalize the concept of probability by defining probability measures on various spaces. In statistics, measure theory helps in understanding the behavior of random variables, distributions, and limits of sequences of random variables. By using measure theory, we can establish important results like the law of large numbers and the central limit theorem, which are essential in statistical inference and hypothesis testing.