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The normal distribution is commonly used in quality control because it is foundational to many statistical quality control techniques. Many attributes and measurements in production processes tend to cluster around a central value with symmetric tails, which aligns with the properties of the normal distribution. This distribution allows practitioners to apply various statistical methods, such as control charts, process capability analysis, and hypothesis testing, effectively.

In quality control, the assumption of normality simplifies calculations and provides a basis for understanding variations in manufacturing processes. Many real-world phenomena in the context of measurement errors, manufacturing tolerances, and product specifications can be well-represented by the normal distribution. Additionally, the central limit theorem states that the means of sufficiently large samples will tend to be normally distributed regardless of the shape of the population distribution, thus reinforcing the relevance of the normal distribution in quality management.

The other distributions mentioned have their specific applications but are not as universally applicable in quality control contexts. For instance, the binomial distribution is useful for discrete outcomes, particularly in pass/fail scenarios, but it does not provide the versatility needed for continuous data. The uniform distribution assumes equal probabilities across a range, which may not reflect the actual behavior of many processes. The exponential distribution is typically associated with time until an event occurs,