Statistical quality control (also known as SQC) aims to ensure accuracy of data compilation and its ensuing results. More specifically, this term refers to the set of statistical tools which professional statisticians use to ensure this accuracy. Some of the ways in which this is accomplished include sampling and probability.

Types of Statistical Quality Control

Carefully designed experiments come into play, as they are utilized for the purpose of process design, product and procedure development, and improvement. This makes statistical quality control particularly useful in the world of manufacturing. However, many state-of-the-art techniques currently used in this field are relatively new, and provide even greater accuracy during studies.

Since the field's birth in the 1920s, three broad categories of statistical quality control have emerged. Descriptive statistics focus on quality characteristics and relationships between numbers. Examples of descriptive statistics include the mean, range, distribution, and standard deviation.

Statistical process control (also known as SPC) occurs when a random sample generated from a process is closely inspected. SPC then determines whether or not the process produces results which then fall within the range previously predicted. A third type of statistical process control is acceptance sampling, where a sample of goods is examined to find out whether the results dictate that a particular batch of goods is acceptable for sale.