How to test relationship between weight gain and light exposure in crickets I am a biology major, and will be running a study and am unsure of what statistical test to run. Here is a summary of what we will be doing:
We are testing to see if there is a correlation between weight gain and amount of light in crickets. Using 20 crickets, they will be split evenly into two groups: a control group getting normal daylight hours, and an experimental group that will be in total darkness. The project will go on for 2 weeks, and each cricket will be weighed once a day for the 14 days. I imagine the variables can be plugged in as idno (1-20), light (1=control, 2=dark), and day (1-14, each weight will be recorded here). 
So, which test should I run? I thought possibly a repeated measures ANOVA but after running, that, it doesn't seem to be testing for what we're looking for. To state it again, we are looking for a significant difference in weight gain between crickets kept in normal lighting conditions and those kept in 24/7 darkness. 
 A: Since the 2 groups are not paired, paired t-test would be inappropriate. Since the number is relatively small, a non-parametric test would be better. Since weight gain is the issue, final weight gain (at 14 days) can be taken ignoring values taken during the study.
Mann-Whitney U test for unpaired data could be applied here for 'weight gain at 14 days' in dark vs light groups. http://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test
A: I'd agree with your gut thought of a 2 factor repeated measures ANOVA, where you are interested in the outcome of the treatment/control effect or, depending on your research question, the interaction effect between time and treatment/control. Such a design may have limitations, though, especially with your proposed sample size... Another thought could be a t-test with "weight gained" as the dependent variable and treatment/control as the independent variable.
A: If you are considering using pre/post scores instead of all 14 data collection points (seems like it would still adequately answer your question), a discussion of possible methods for analysis occurs on the following thread: Best practice when analysing pre-post treatment-control designs.
