I have a set of data that was recorded from an engine that we are testing. We've noticed lately that a particular pressure value will sometimes spike with no apparent explanation, as seen in the attached graph. The pressure in question is passively regulated by a pump, but it is also dependent on operating factors in the engine. I'm trying to narrow down whether this spike is indicative of a problem with the pump or if there's something else wrong with the engine to cause this spike. Plotting the pressure against other measurements hasn't really helped, but I wanted to see if there's a way I can statistically measure the significance of changes that occur, or whether it's just typical variation. I recall from Stats that my professor used the Student's T-Distribution to measure whether a change between two data sets (in my case, before and after the pressure spike) was statistically significant, but when I tried it in Excel (using this tutorial, it gave me exactly a 100% probability that this pressure spike occurred by chance (we know from testing that's not the case). I've since read that the T-distribution is typically used when the standard deviation for a sample isn't directly measurable, but I do know that from the data, so is there another way to go about this? EDIT: Just to clarify, to obtain my T-dist value, I set mu = average pressure before the spike, xbar = average pressure after the spike, s = standard deviation of pressure after the spike, and n is the number of data points after the spike (201 in my case). I plugged that into the T-value formula and got 213.4, and the T-dist probability for that is 1.