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Normal distribution is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. It is characterized by its bell-shaped curve, defined by its mean and standard deviation. Many natural phenomena, such as heights or test scores, tend to follow this distribution, making it a fundamental concept in statistics.

The Central Limit Theorem states that the distribution of the sample means will approach a normal distribution as the sample size increases, regardless of the shape of the population distribution. This theorem is crucial for making inferences about population parameters based on sample statistics, especially when dealing with large samples, as it justifies the use of normal distribution in various statistical analyses.

Measurement error refers to the difference between the actual value and the value obtained through measurement. In the context of the exact volumes of Coke in 12 oz cans, slight variations in filling processes can lead to a distribution of volumes that approximates normality due to the aggregation of these small errors. Understanding measurement error is essential for interpreting data accurately and assessing the reliability of statistical conclusions.