Really enjoyed the latest episode on the importance of truth in cancer research. It was not only informative but also entertaining. Your dedication to transparency shines through, making a complex topic accessible and engaging. Thanks for casting a light on the crucial facts and questioning the credibility where it's due. Keep up the great work!
These are very funny stories about how to make sure you get the results you want. I would never have thought to do these. Perhaps I have too much belief that we should be doing good science. It does remind me, however, of a situation my sister ran into in graduate school. A sociologist had developed a study which asked very ambiguous questions of non-native English speakers. The results were not what he wanted, no matter how he interpreted the answers. His solution? Change the statistical test! It has been 40 years since she described it to me. I can't recall if he went from chi-squared to a 't' test or Poisson, but the alternative test made no sense for the data being analyzed. But it somehow gave him a statistical significance to prove his hypothesis....
Really enjoyed the latest episode on the importance of truth in cancer research. It was not only informative but also entertaining. Your dedication to transparency shines through, making a complex topic accessible and engaging. Thanks for casting a light on the crucial facts and questioning the credibility where it's due. Keep up the great work!
These are very funny stories about how to make sure you get the results you want. I would never have thought to do these. Perhaps I have too much belief that we should be doing good science. It does remind me, however, of a situation my sister ran into in graduate school. A sociologist had developed a study which asked very ambiguous questions of non-native English speakers. The results were not what he wanted, no matter how he interpreted the answers. His solution? Change the statistical test! It has been 40 years since she described it to me. I can't recall if he went from chi-squared to a 't' test or Poisson, but the alternative test made no sense for the data being analyzed. But it somehow gave him a statistical significance to prove his hypothesis....