In preparation for this assignment, I read an article titled The controversy of significance testing: misconceptions and alternatives. As the title suggests, the article went into some detail regarding the controversy surrounding significance testing. The main controversy was the idea that the P value is often misinterpreted and that other factors such as confidence intervals and effect sizes are ignored. This point was interesting and reminded me of something from the textbook. The idea that just because a result is statistically significant does not mean it is important. While I still believe proper experimental design and statistical analysis are important, I could identify with this critique regarding the misinterpretation of P values and what they really mean. It is frustrating to think that after all the time planning and executing an experiment with a statistically significant P value, that the result was really irrelevant. The book gave the example of a drug that led to a decrease in symptoms with a statistically significant P value. However, the statistically significant result only decreased symptoms by 7%, which was not enough in the broader scheme of things. Because my research is related to the effects of a certain compound on cancer cell growth, a statistically significant result that would not really benefit patients isn’t ideal.
In favor of statistical testing is first the fact that this article was written in 1999 and things have since improved with the reliability of statistically significant outcomes. Furthermore, in order to properly run an unbiased experiment, the design must be planned first, improving the integrity of the science conducted. I think the thought that must go in to properly performing research leads to better execution of science, and if more people prepared correctly, it may improve issues with reproducibility in science. Overall, I believe the benefits of statistical testing outweigh the drawbacks. When done correctly, the outcomes of research are more reliable.