My other recent data collection project had to do with the elevators in my building. It’s only 13 floors, but it has five elevators. Seems like a lot. More than a third of the time, you’d expect one to be waiting for you on your floor, though they never are. I started noticing that two of the elevators rarely if ever catered to my vertical transportation needs. So I used Counter to collect some data to see if, indeed, those two elevators were less likely to pick me up.
I plugged the data into PSPP and ran a chi-square analysis and, indeed, my data is only 0.03 percent away from giving me 95% certainty that the last two elevators are less likely to be the ones opening for me (p = 0.0546).
However, a funny thing happened partway through the data collection period. For the first several weeks of data collection, the last two elevators never open for me, not even once. Limiting the analysis to only this portion of the dataset shows with near 100% certainty (p < 0.0001) that the last two elevators were opening at a different rate than the other three.
But then one day I pressed the button and, voilà, elevator four opened. Ever since that day, the last two elevators have been appearing at the same rate as their brethren (p = 0.9613)…
…making it perfectly reasonable to conclude that whatever was wrong with elevators four and five has been fixed.
One interesting dynamic today with statistics tools is that there are lots of specialized statistics calculators out on the web today. The advantage of a specialized online calculator is that the interface can be customized for that kind of analysis. At times, particularly with a quick analysis of a small dataset, this can be a lot easier than feeding the data into a generalized tool like PSPP.