Statistical process control was pioneered by Walter A. Shewhart in the early 1920s. Shewhart created the basis for the control chart and the concept of a state of "statistical control" by carefully designed experiments. While Dr. Shewhart drew from pure mathematical statistical theories, he understood that data from physical processes seldom produces a "normal distribution curve" (a Gaussian distribution, also commonly referred to as a "bell curve"). He discovered how the observed variation in manufacturing data did not always behave the same way as data in nature (for example, Brownian motion of particles). Dr. Shewhart concluded that while every process displays variation, some processes display controlled variation that is natural to the process (common causes of variation), while others display uncontrolled variation that is not present in the process causal system at all times (special causes of variation). Uncontrolled variation is often associated with defective products, providing a data-driven means to identify problems and improve quality.