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I am trying to use bootstrapping to enable the comparison of gait curves (specifically knee angle during a gait cycle) between two groups of subjects. Essentially, I was to see at what points in the gait cycle (from 0-100%) the two groups differ. Because this is an 'unpaired' analysis, I believe I need to use a stratified bootstrapping as described in the answer to this question. I have also read Michael Chernick's book, but am having trouble translating the stratified example into practical analysis.

My problem is that when I run the stratified bootstrapping I end up with very tight confidence intervals that are significantly tighter than the confidence intervals of either of my initial datasets. My understanding is that I can consider any position along the x-axis (time) in which the difference between the means of the two datasets (including the confidence intervals) does not include 0 as significant. However, with such tight confidence intervals, this seems to grossly overestimate significance. What am I doing wrong that leads to such tight confidence intervals?

To clarify, here is my experimental setup: Data set 1: Gait curve from population A. m subjects by n values over time; m=30 Data set 2: Gait curve from population B. m subjects by n values over time; m=30

Because the data are from different populations, they are "unpaired." Thus, I am trying to run a stratified bootstrapping algorithm. First I run bootstrapping on Data set 1 with 1000 samples. Then I run bootstrapping on Data set 2 with 1000 samples. Then I subtract the mean curve for Data set 1 and for Data set 2. I save this mean difference curve. The whole process is repeated 1000 times, and then I run the bootstrapping on these 1000 samples x n positions over time. Finally, I am looking at the mean and confidence intervals from this bootstrapping. Here is where I have incredibly tight confidence intervals, much more so than either of my initial data sets, or the confidence intervals from the initial two bootstraps. Any suggestions would be welcome!

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