The graphic [\ref{fig:comp_sigmasked}] here is created only for the composite mean this time. The other values are just as potentially informative, but as the mean is the most commonly reported and requested statistic, it seems worth concentrating on it for the moment¹¹As in, I haven’t scripted up a routine to do the significance testing of the other statistics yet.. Blocking out ’insignificant’ values [\ref{fig:comp_sigmasked}] doesn’t drastically change the impression we had before [\ref{fig:comp_examples}]. However it does emphasize some of the nuances in the calculation.

It’s important to note that the significance testing method used here is rejection based. That is, we first select a threshold, in this case a commonly used, but arbitrary, one. Then mean anomalies that aren’t large enough in magnitude, that is aren’t adequately strongly positive or negative, are rejected. This let’s us determine where we have reason to be confident that there is an association between El Niño and rainfall. However, the approach doesn’t distinguish between areas where we could be confident that there isn’t an association and those where there just isn’t enough data to tell. It also doesn’t say how confident we are in the particular value that has been selected to represent the association. To make those statements would require a different approach.