Hi,
I am hoping to get some help on what type of statistical test to run to validate my data. I have run 2 ELISAs with the same samples for each test. I did perform a Mann-Whitney U-test to compare the groups, and my results were good.
However, my PI wants me to also run a statistical test to determine that there wasn't any significant difference in the measurement of each sample between the runs. He wants to know that my results are concordant/reproducible.
I am trying to compare each sample individually, and since I don't have 3 data points, I can't run an ANOVA. What types of statistical tests will give me that information? Also, is there a test that will run all the samples simultaneously but only compare across the same sample.
For example, if my data looked like this.
A: 5, 5.7
B: 6, 8
C: 10, 20
I need a test to determine if there is any significant difference between the values for samples A, B, and C separately and not compare the group variance between A-C.
If you want to see how comparable the results from the two ELISAs are, simply plot the results of the first ELIZA against those of the second ELISA.
Another option is to make a mean-difference plot (aka "Bland-Altman plot"): plot the differences between the ELISA results against the mean of the ELISA results.
Doing a statistical test and interpreting a non-significant result as "there is no difference" or "the groups/runs/ELISAs are comparable" is logically flawed and complete nonsense. Don't do this, ever!
I looked into the Bland-Atman plot, and it's not exactly what I am looking for; maybe I can explain it a little better.
I am looking to determine if the variability between the repeated tests is within a normal range. For example, in my above example, the difference in results between each replicate for sample A is 0.7, but the difference for sample C is 10. I would assume that some error occurred in sample C for the results to be double after repeating the same test with the same protocol. However, I am not sure if a difference of 0.7 is within a normal range of variability or not.
Is there a statistical way to test this or is this more theoretical?
No, there is no way to test this statistically. This is a subject matter question.
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