Issue link: https://www.massageandbodyworkdigital.com/i/72195
SOMATIC RESEARCH had no effect on their measurements of quadriceps pain, strength, or function after the exercise. We'll get back to the larger implications of those findings toward the end of the article, but here, we'll just talk about the null hypothesis. A goal of a research study is to try to correctly determine whether or not to accept or reject the null hypothesis—neither to accept it mistakenly (false negative) nor to reject it mistakenly (false positive). To see how that works in practice, we'll switch from sports massage to cardiac surgery for a moment, since a particular research article demonstrates clearly how the researchers calculated a power analysis for their study. Hattan's ("The Impact of Foot later discussion, researchers look at the evidence to see whether it calls for rejecting the null hypothesis and supporting their own hypothesis. For example, if a researcher hypothesizes, like Jönhagen's ("Sports Massage After Eccentric Exercise") team did, that "Sports massage can improve the recovery after eccentric exercise,"3 then the null hypothesis would be something like "Sports massage has no effect on recovery after eccentric exercise." All of these concepts come back, ultimately, to whether to accept or reject the null hypothesis. As it happens, Jönhagen's team did end up accepting the null hypothesis and rejecting his research hypothesis, because they found that the massage Massage and Guided Relaxation Following Cardiac Surgery: A Randomized Controlled Trial") research team investigated whether foot massage and guided relaxation promoted calmness (among other measures) in cardiac surgery patients. Their description of how they determined the ideal sample size for their study points at the multiple factors involved: "A post hoc [carried out after the study] power analysis test suggested that a sample size of 45 would be required to detect a difference of the size observed with an acceptable level of Type II error [false negative] (power = 0.8)."4 From this statement, we can see that statistical power has to do with detecting an effect, with the size of a sample, and with how much risk of error we're willing to tolerate. In the literature, you'll often see it written in a much shorter way, but Hattan's description shows details of what is involved in a power analysis—sample size, effect size, and acceptable tolerance of error. One way to think of it is, how large a population do you need to make sure you see an effect that is there—that you don't make a false negative error by missing something? If it's a large effect, you probably don't need as 140 massage & bodywork september/october 2008 many people to see it as you do if it's a small effect—in other words, if it's something that could be easily missed, you improve your chances of seeing it by looking for it in more people. But if it's a major effect, it will probably show up more dramatically, and you can see it in fewer people. For that reason, increasing sample size is a very common way of increasing the power of a test. So where did that often-mentioned number 35–40 for massage studies referred to earlier come from? It's an estimate that probably came out of one particular study as having sufficient power in that context, and was then accidentally generalized into a more universal number that is sometimes quoted as applying to many massage research studies. But since a sufficiently large sample size depends on the size of the effect being looked for, and how much risk of error the researchers are willing to accept, it really depends on the question being researched. When researchers design a study, they put a lot of time and effort into the question of how many participants to include, and they consult statisticians to determine that number, because they know that funding agencies will examine it carefully to determine whether they've gotten it right. There's no "one size fits all" number that massage research studies should have to ensure sufficient power. Instead of trying to come up with such a number for all studies, a better strategy is to follow the researcher's logic, as explained in the article, for why that particular number was right—ensured sufficient power—for that study on its own terms. If the researchers' explanation of how the sample size was chosen makes sense, it's probably worth trusting for evaluating that article. If it doesn't make sense, or if it is not explained