Tuesday's Class

I really enjoyed class this Tuesday. Rebecca did a really great job of putting it all together. It was very useful to define a study, hypothesis, and design according to whether the dependent and independent variables were continuous or categorical. The exercise actually made us think about relevant biological variables and how we should test them. I learned a lot about when to use different tests. Ultimately, this will be very helpful for my independent project/honors thesis. Now if we could just do this with a Thursday lab, I feel like I would get so much out of R. By the way, based on the biostats test, I found R to be useful in running analyses but inputting data was a nightmare. Anyone have any good tips or is it just hard to do? It is just not as friendly as a spreadsheet like Excel.

JV Ch. 6

I found this chapter to be a little more enigmatic than previous ones. There seemed to be a lot of emphasis on trying to do exercises that supported the Central Limit Theorem. This exercise was less practical and a little too redundantly didactic. What can we actually use from this chapter? I guess the most useful thing to me was learning to do the loop. I figure that this will come in handy in the future. I guess I would like to see this course take a step towards practically applying the information in JV to a biological study. Is there anyway that we could use a different biological data set and manipulate it with what we learned in each JV chapter? I think that I would learn a lot more from this and be more motivated in doing the exercises. After all, the purpose of this course is to learn how to use statistics in our projects and studies, and as of now I don't think I am anywhere close. Let's put JV in a different context!! I don't want to learn about R for the sake of doing just that. I want to use R as a tool in my own studies, and the examples in the book have so far been too theoretical.

Spiders and Stats

I think that I have redefined my independent project pretty well (I hope, see last posts), but I guess I need to start thinking about what kind of statistics I want to use. I was thinking about some t-tests, chi-square, and maybe ANOVA. I guess I am just not all that familiar with trying to determine what kind of stats to use. Usually, people just tell me like for example, in ecology lab. I am sure that I will want to show a linear regression for number of sperm released per intromission, which I can find out by dissecting some males and counting sperm. But I am not sure how to compare number of offspring for each group (1. sterile, sterile 2. sterile, fertile 3. fertile, sterile 4. fertile, fertile) especially with varying numbers of sperm in each group. This would involve allowing each male equal number of intromissions and then varying the ratio of intromissions between first and second male(ie: 1:10). Does anyone have any suggestions?? Maybe if my hypothesis that the priority pattern is determined by a sperm number (more of a lottery, vs. placement of sperm) is supported, I could show that there is no difference between a fertile first male with a 10:1 advantage of sperm number and a fertile second male with the same advantage. I don't know, help!!

Evolutionary Psychologists Gone Awry

In my evolutionary psychology class, we have been looking at same papers attempting to explain short and long term mating strategies as well as differences in sexual jealousy among the sexes. It has been an extremely interesting pursuit and the starting theories have been very logical and well placed in the framework of sexual selection. The issue is that the data sets they are picking out to support their theories are not necessarily representative of a population. They are not so random so to speak. Also, some clearly seem to be interpreting data in light of their theory in a very subjective manner. The methods one uses and the statistics are important as well! You can have a brilliant theory, but it will not be well respected unless the sampling is random and unbiased and the interpretation is not skewed. It is a little scary to think about especially because human psychology is so programmed to see a pattern even when there is not one!