# A crisis of confidence (intervals)

Lately I’ve taken to exploring some of the aggregated event statistics that we
have on file at Flight Data Services — and as a side project, I was using my
knowledge of statistics to develop a fairly straightforward anomaly detection
algorithm. My first approach was to compute the daily average of a particular
key point value (in this case, `Acceleration Normal at Touchdown`

), and then
compute the mean and an arbitrary confidence interval (say, 99.5%). Data that
fell outside of this interval would then be marked as an anomaly and would
warrant further investigation. This is a pretty rudimentary statistical
approach to anomaly detection, but one that’s commonly applied in this context
— the only problem is that *it’s wrong*.