tag:blogger.com,1999:blog-7310946608587805029.post2547778162037462955..comments2024-03-13T01:48:29.943-04:00Comments on Unbiased Research: One-tailed or two-tailed?TJ Murphyhttp://www.blogger.com/profile/17292359594683490598noreply@blogger.comBlogger7125tag:blogger.com,1999:blog-7310946608587805029.post-3851990055719120162016-05-06T01:15:12.531-04:002016-05-06T01:15:12.531-04:00I would say the test you choose really depends on ...I would say the test you choose really depends on the question you ask. For example, there are three types of clinical trial: The superiority trial, the non-inferiority trial, and the equivalence trial. For the first two, the hypothesis is one-sided, because you expect the new drug brings the patients a better / not-worse outcome (either longer survival or lower mortality) than the standard treatment or placebo. For the last one, the hypothesis is two-sided, because you expect the new drug gives the same effectiveness as the standard treatment.<br />All the hypothesis testing should be started from the hypotheses. Once you formulate the statistical hypothesis, the test is accompanied. From a very practical view, I would say, generally, for a pilot study, a two-sided test is appropriate if we have no idea what the direction of change will be. For a confirmation study, which is usually established on the previous works, a more clear statistical hypothesis is available, and your test should follow the hypothesis you propose.Tigerhttps://www.blogger.com/profile/09845680948434368064noreply@blogger.comtag:blogger.com,1999:blog-7310946608587805029.post-45254561837413492482016-05-06T01:14:36.144-04:002016-05-06T01:14:36.144-04:00This comment has been removed by the author.Tigerhttps://www.blogger.com/profile/09845680948434368064noreply@blogger.comtag:blogger.com,1999:blog-7310946608587805029.post-34972544348564038862016-04-28T12:28:39.204-04:002016-04-28T12:28:39.204-04:00I, also, agree with your past experiences. It seem...I, also, agree with your past experiences. It seems that any time I have done statistical analysis in the past, it has been a two-tailed test. I can imagine this is because labs are always looking to get the most out of their data and, as you descirbed, not waste time, money, animla lives, or precious samples. Science is theoretically there only to increase our knowledge on a particular subject, and so I see no harm in using statistical methods that will "kill two birds with one stone," so to speak. If anything, its almost a shame to not look at both sides of possible responses every time. That being said, this position sightly lends itself to the discussion of publication of "negative" data... while it is highly important to know that something increases (or decreases) an effect, it is just as important to know that there is no effect. Anonymoushttps://www.blogger.com/profile/05639191621314535530noreply@blogger.comtag:blogger.com,1999:blog-7310946608587805029.post-28662231321801501512016-04-21T19:47:29.363-04:002016-04-21T19:47:29.363-04:00From my very limited experience I think that the d...From my very limited experience I think that the decision to do a one tail vs 2 tail test depends on how much confidence you have in your initial hypothesis will be correct. There's always going to be some amount of 'unknown' quantity (otherwise why do the experiment), which increases the less data that is out their to support the hypothesis. However, if you can make a compelling argument based off of some previous data then I think going with a one tailed test is alright in some instances. Anonymoushttps://www.blogger.com/profile/17025616667172530247noreply@blogger.comtag:blogger.com,1999:blog-7310946608587805029.post-80579694990989298802016-04-21T19:47:24.306-04:002016-04-21T19:47:24.306-04:00From my very limited experience I think that the d...From my very limited experience I think that the decision to do a one tail vs 2 tail test depends on how much confidence you have in your initial hypothesis will be correct. There's always going to be some amount of 'unknown' quantity (otherwise why do the experiment), which increases the less data that is out their to support the hypothesis. However, if you can make a compelling argument based off of some previous data then I think going with a one tailed test is alright in some instances. Anonymoushttps://www.blogger.com/profile/17025616667172530247noreply@blogger.comtag:blogger.com,1999:blog-7310946608587805029.post-37729407570518038612016-04-21T16:56:48.594-04:002016-04-21T16:56:48.594-04:00I think you raise an interesting point in this pos...I think you raise an interesting point in this post. I was also taught that your "default" should be two-tailed in order to cover all your bases, so to speak. There are many things in our field especially that are so intertwined that your expected result is often not the result that you see in your data. I have always been taught to let the data speak for itself, and choosing a one-tailed test without absolute certainty (i.e. previous experimental data and a superior knowledge of the system), you are giving the data a microphone and and speech to read. I think that in well-defined systems and perhaps repeat experiments, it's great to use a one-tailed test and can clearly boost your analysis. However, in a system that is highly complicated or a really wide open question, even if you have a hypothesis that is supported by some kind of previous observation from your lab or others, it sounds like it would be wise to use a two-tailed test.Madeline Pricehttps://www.blogger.com/profile/02881526474612085058noreply@blogger.comtag:blogger.com,1999:blog-7310946608587805029.post-14336995039315363192016-04-19T17:07:50.241-04:002016-04-19T17:07:50.241-04:00I'm not dismissive of one tail or the other. I...I'm not dismissive of one tail or the other. I say pick the tail that makes the most sense, scientifically.TJ Murphyhttps://www.blogger.com/profile/17292359594683490598noreply@blogger.com