The chapter Jumping to Conclusions, is about how scientists would really want their experiments to work so they would forget to correct and recheck their work. For example, in 2002 the Food and Drug Administration (FDA), the National Institutes of Health (NIH) created a test that would allow them to find ovarian cancer in a much easier and safer way. This test would give people the option of avoiding surgery to rule out the possibility of cancer. The test was a huge breakthrough many women were learning they’d have the disease at its much later stages (pg 123). The scientists were getting so much attention and being recognized for this new test. Many people were ecstatic waiting for this option of using the test to become available. This test was more advanced then other tests instead of looking for one particular molecule it searched for a more broader range of patterns and protein spectrum. “It seemed like the start of something big. Really big” as Richard Harris put it. (pg 124).
Baggerly another scientist showed a very special interest in this test and he wanted to see if his data would match the same data the researchers were getting, if he decided to recreate the test. Baggerly recreated this test step by step but, something wasn’t adding up. He stated “we couldn’t find the patterns they were reporting” (pg 124). This is just one of many endless examples of researchers jumping to conclusions after one good outcome. This is all very common in the science world and happens in many studies. When this happens it’s referred to as “the batch effect” (pg 125). Basically, what I learned is to double check all the outcomes of your work and don’t believe something without looking at proven facts.
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