Prove me wrong-Leia P.

Chapter 6,Jumping to Conclusions, focuses mainly on the current study on Ovarian cancer and how results are determined using the p-value. Baggerly had two groups of women, 50 who had ovarian cancer and 50 who did not.

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The goal at the time was was to see if there were any differentiating patterns among the proteins in the samples. After looking for one of his articles I found that he believed that miRNA had a big part to play in ovarian cancer. Unfortunately after Baggerly collected his data it was later realized that the results,”It reflected the ‘noise’ generated by the machine, he concluded, and had nothing to do with detecting a real protein fingerprint for ovarian cancer.”-pg 125. All the research had gone to waste due to Baggerly the testing both groups on the same day. If I were him I would’ve taken my time to make sure that there were not any other variables that could potentially mess up my results. There are so many little things people could miss that in the end make a big difference. Personally I never hear much about the current progress going on with things such as ovarian cancer. Breast cancer is what people always support an talk about in the media so I think it would be interesting if we could talk about ovarian cancer in class. I want to learn how that certain cancer harms the body and how common it is in women.

After going a bit a further into the chapter, I found myself surprised with how researchers determine their success. There is something called the p-value.


“The conventional (but wrong) understanding is that a study finding reaches statistical significance if there’s 95 percent chance that it is correct only  a 5 percent chance that it is wrong.”-pg 134. This would mean scientists are taking short-cuts to determine he effectiveness of their research. They figure that if they have gotten the information right up 95% so far then the chance of them being wrong in the end is very slim. However that 5% chance is still there and I am not the type of person who would want to ignore it. Although some believe that the p-value is being misused (as explained in this article) now scientists are considering  going to .05% or as far as .005%, in hope of making the results more reliable. Unfortunately some scientists are opposed to that idea due to the fact that sometimes it is already difficult enough to reach that 5%.


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