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The Normal Distribution and the 68-95-99.7 Rule (5.2)
https://www.youtube.com/watch?v=mtbJbDwqWLE

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>>1 Normal distribution, also known as the Gaussian distribution or bell curve, is an important concept in statistics for several reasons:

It is a widely-occurring distribution in nature and many real-world phenomena, such as human heights and IQ scores, are approximately normally distributed.

The normal distribution has several useful properties, such as symmetry and a well-defined mean, median, and mode.

The central limit theorem states that the sum of many independent and identically distributed random variables is approximately normally distributed, regardless of the distribution of the individual variables.

Many statistical tests and models assume normality of the data, such as t-tests, ANOVA, and linear regression. This is important because these tests and models are robust to moderate deviations from normality, but can be unreliable if the data is strongly non-normal.

The normal distribution is also widely used in probability theory, and it is the foundation of many statistical methods such as estimation, hypothesis testing and reliability analysis.

Overall, normal distribution plays a crucial role in statistics, it is important to understand the assumptions and limitations of using it as a model.