I'm trying to perform a statistical analysis but not sure what test is most appropriate. The experiment and snippet of sample data are below:
Methods: Bee flights from a beehive were recorded, tracked, and quantified using some fancy software. 2 min recordings were made, every 15 min, over a span of 3 hours, on 2 consecutive days from the same hive. On Day 1 there was a solar eclipse peaking at 3:15 pm. Day 2 was a normal day. At each timepoint we calculated only 1 number. Assumptions are that the number of bees flying at each timepoint is variable, and not dependent on earlier/later times. The hypothesis is that during an eclipse bee flight activity will be different.
Time Day 1 Day 2 1:45 4.825986975 5.011309825 2:00 4.76436878 4.944759887 2:15 4.857806302 4.995020539 2:30 5.160417753 5.098229915 2:45 5.151340989 5.123143909 3:00 5.205157211 5.01482563 3:15 5.576328277 5.060712789 3:30 5.499265895 4.767659804 3:45 5.341123626 4.698898463 4:00 4.511481755 4.336670549 4:15 4.454618556 4.540783545 4:30 4.475789132 4.203964626 4:45 4.378358091 4.239819416
Results: Visually we saw more flights during the onset of the eclipse. When plotted the curves qualitatively show a difference during the eclipse.
What stats test would be correct to run on the dataset to say whether there was a difference between Day 1 and 2 (in other words, did the eclipse meaningfully influence flight activity on Day 1)? I realize there are caveats to the whole setup but what I'm asking is about stats..
Can I use a test to see if there was a meaningful change just during the eclipse Day?
I know the sunlight intensity (irradiance) during the eclipse. Can I do a Pearson's to look for a correlation between intensity and flight activity?