Wheeler - Common Cause and Special Cause
Simply put, most people don’t understand variation. I highly recommend everyone read Understanding Variation by Donald J. Wheeler. It is the best business book I have ever read!
According to Donald J. Wheeler differentiating between common and special causes of variation is essential for data analysis. Building on Walter Shewhart's concepts, Wheeler refers to common cause variation as "Routine Variation" and special cause variation as "Exceptional Variation".
Dr. Walter Shewhart’s two rules for presenting data form the basis for honest statistics. You should follow them:
Present data in a way that ensures all predictions that may be made can be made. Provide a table of data, graph, and context. Data divorce from context is in danger of distortion.
Never mislead people into taking actions they wouldn’t take if data were presented in a different form. Averages, ranges, and histograms summarize data, but they also obscure the time-order. If the time-order shows a definite pattern, don’t obscure it. Always include time series graphs when summarizing data.
These two rules can be summarized into one principle: No data have meaning without context. Don’t trust anyone who cannot, or will not, provide context for their metrics.
All data contains noise, some data contains signal. But before you can detect signal, you must filter out noise. This is why control charts are a more powerful form of data analysis than the traditional approaches.
