Root cause analysis Rule #2

Correlation

Another classic mistake in Root Cause Analysis is to believe that correlation implies causation. It does not. In simple words, the observation of 2 events happening almost at the same time or 2 variables going up and down together is not enough to prove that one is the cause of the other. This is a bit tricky because some statistical tests (e.g. regression analysis) used in Lean or 6 sigma look for correlation and it’s easy to go too far and assume causation, but this is wrong.

This is an example. The daily average temperature (ºC, ºF) is correlated with the number of people (#) suffering a heart attack that day (and, probably, there is also a cause-effect relationship). The daily average temperature (ºC, ºF) is correlated with the amount of ice-cream (Kg) consumed that day (and, again, there is probably also a cause-effect relationship). However, the amount of ice-cream (Kg) consumed one day is correlated with the number of people (#) suffering a heart attack that day, but the probability of cause-effect relationship is low:

IMG_4463

Find more examples here: http://tylervigen.com/spurious-correlations

Some say that real causation can never be proved and therefore we only see correlation and imagine causation (more on this here). We can intuit that a cause-effect relationship is stronger than simple correlation. Anyway this belongs to the field of philosophy and in most cases common sense and observation can help us take an educated guess. The use of Ishikawa diagrams, 5 whys, DOE and PDCA experiments, besides the implication of the true process experts (those who work everyday there where the problem happened) can make a difference.

Picture from: http://jwilson.coe.uga.edu/EMAT6680Fa11/Shannon/STATS/Comics.html

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