1. Analyzing the grass shrimp data from Wednesday’s lab Step one: make 3 graphs, each with “Number of cysts” on the x-axis Step two: do pairwise t-tests or one- factor ANOVA to look for differences.

2. Making the graphs (step one) You have eighteen groups of data: BEHAVIORWATER#CYSTS % time movingControl vs. KillifishNone, Few, Lots # risesControl vs. KillifishNone, Few, Lots # tailflipsControl vs. KillifishNone, Few, Lots Gier calculated the averages of each, and is pretty sure the following table of averages is correct: # of cysts% moving# of rises# of tailflips None65.482.68.4 1 to 570.583.37.68 >581.744.68.63 # of cysts% moving# of rises# of tailflips None62.274.14.55 1 to 568.123.05.63 >580.285.61.39 At left: CONTROL At right: KILLIFISH

3. On a blank worksheet, create mini-spreadsheets of the averaged data, analyzing just one behavior at a time. Example: the “% time moving” data looks like this: Highlight these cells and do a bar graph (“column chart”) #cysts %time moving, control % time moving, killifish none65.4862.27 1 to 570.5868.12 >581.7480.28 Next: two more graphs like this. Then: decide which pairwise t-tests or single- factor ANOVAs to perform.

4. If you want to try single-factor ANOVA, here is how to organize the data: % time moving, no cysts % time moving, 1-5 cysts % time moving, >5 cysts ### ### ### #... Each # is the data from an individual shrimp. In your data analysis window, you can do a single-factor ANOVA, which is roughly the same as doing all possible t-tests.