Errors are assumed to be normally distributed because of the central limit theorem, which states that the sum of independent random variables tends to be normally distributed, regardless of the distribution of the individual variables. The error term in a model is essentially a sum of individual errors, so it is reasonable to assume that it would also exhibit a normal distribution. Additionally, many real-world phenomena can be modeled as normally distributed, making it a commonly used assumption in statistical analysis.
