I have studied the effect of hyperglycemia on subarachnoid hemorrhage (preclinic) and I used PET and MRI for this purpose. I am now comparing the data between hyperglycemic and normoglycemic animals on days 1 and 3 (I measured the volumes of damage). For doing the statistics, I am using GraphPad prism. I put all my data in columns, I performed a normality test and they do not follow a normal distribution so the parametric analysis is automatically dismissed. Then, I performed a Kruskal Wallis analysis but, here is my concern:
As I am interested in assessing the difference between hyperglycemia and normoglycemia (differences on day 1 and differences on day 3), I thought that maybe a Mann-Whitney test choosing the variables two by two would be correct as well (I have two groups, not four). What do you think? Is it correct?
Thanks in advance!!
Hi, Ana Joya . I think what would be helpful is if you can share the setup for your data. Like, specifically what columns you have, and the types of values you have in them.
You can just make up some data, but be sure that the values are structurally similar to your actual values. I mean, what kind of variable is "Volume" ? Is it numeric with several significant digits ? Integer ? Ordinal ?
Also, do you have individual subjects identified on Day 1 and Day 3 ? That is, you can say, "Subject A had this measurement on Day 1, and Subject A had this measurement on Day 3" ?
"I performed a normality test and they do not follow a normal distribution so the parametric analysis is automatically dismissed." Real data will never be exactly normal (John Tukey's (\citeyear{Tukey1986}, p.~72) argument that researchers should ``move from [the idea that] all assumptions are right towards all assumptions are wrong.'') You should say more why you don't want to estimate parameters. What transformations have you explored other than ranking?
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