Three are 8 common "statistical traps." A common theme is to think about the process, or the sequence in which the data was gathered. You can get variation in inputs from: people, methods, machines, materials, measurements, and environment.
Trap 1: Treating all observed variation in a time series data sequence as special cause. Is the process that produced the first number different from the process that produced the second number?
Trap 2:Fitting inappropriate trend lines to a time series data sequence. It is not always right to find the regression between two points, if the two points are from two different processes. You have to use process-oriented thinking. More changes happen in a step fashion than linear trends.
What is a trend? A trend indicates a trend in the process average. A trend is a sequence of 7 or more points continuously increasing or continuously decreasing.
Trap 3: Unneccessary obession with the normal distribution. Other common distributions include the binomial (I used that one once!).
If 51 people flipped a coin 50 times, the average number of heads would be 25. However, the individual numbers would range between 14 and 36, a 2.5 fold difference. But looking for outliers is not appropriate, because everyone had the same process and looking at outliers does not change this fact.
Trap 4: Inappropriate calculation of the standard deviation and "sigma" limits. If you do look for outliers, try using 3 standard deviations instead of 2. If you use 2, there is a 92.7% change that at least one of the 51 coin flippers would be treated as a special cause when in fact he was a common cause, just by accident. Using 3 standard deviations changes that to 14.2% chance that only one person would be mistakenly identified as a special cause.
Try using a moving range, or even better, a median moving range, which is more robust to "shifts" and special cases. The median moving range tells us how far apart any two consequetive points need to be before declaring a special cause. The equation is 3.865 x (median moving range).
Trap 5: Misleading special cause signals on a control chart
Before you start finding special cases everywhere, check out if the process has changed. If the process has changed, then the process average has also changed, so the old control limits are no longer valid at the new process levels.
Trap 6: Choosing arbitrary cutoffs for "above" average and "below" average
Thi is a health system ranking system, in which hospitals are ranked by if people would definitely recommend, probably recommend, probably not recommend, and definitely not recomment the hospital to their friends. It is zeroed in a the probably nots and definitely nots.
Trap 7: Improving processes through the use of arbitrary numerical goals and standards
It seems like region 1 is doing the best, but it is not correct to say that it is completely due to the methods of the region. After all, region 1 seems "predestined" to meet the goal, but it is because of its inherent properties, management, workers, or pure luck? Why not analyze each work unit within each region?
Trap 8: Using statistical technqiues on "rolling" or "moving" averages -- a lurking trap
The first graph shows the raw data. The second shows a rolling average of 4 or "four quarter rolling average", meaning that every 4 observations is averaged. The second shows a twelve month rolling average, and the last is a 52 week rolling average.