Fooled by Randomness

This is a nice article discussing how people are often fooled by thinking there must be special causes for patterns in random data. I still remember my father showing my classes these lessons when I was in grade school. Playing At Dice – What That “Weekend Exercise” Was All About:

Yes, that’s more or less the point. If the system is behaving statistically, it will show apparent sequential trends that in reality are mirages. The dice experiment demonstrates that – and if you look at statistical and sequential temperature data, you see the exact same behavior!

When people are asked to explain random variations in data they will make up special causes (that they often even believe are special causes even when they are not) but you can improve management a great deal by just stopping the requirement to “explain” common cause variation (which in practices mean to claim a special cause for the common cause variation). Use that time instead to standardize processes. Create control charts for critical processes. Run experiments using PDSA cycle

Related: Seeing Patterns Where None ExistsUnderstanding DataOperational Definitions and Data CollectionRed Bead Experiment

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