Hello all,
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SEE THE FITH POST OF THIS THREAD TO GET A BETTER IDEA OF THE ISSUE
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I am comparing three datasets of length 7201 datapoints each (on images this is the x-axis values). At every one of the 7201 datapoints I have 989, 1003, 945 trials each (on the figures this is the z-axis values). think of it as a matrix, 7201 x ~1000 for each dataset. I take the means and standard deviations in the '1000' direction to provide a 7201 x 1 array for the mean (then do the same for the Std.Dev). Now when I plot this data they overlap according to the following figure (data-means.jpg), the top hump is the mean+1\sigma, the bottom hump is the mean-1\sigma, and the middle hump is the mean of each dataset.
So graphically I can conclude that the three datasets are the same, however when I conduct the two tailed t-test (as well as the F-test of variance) comparing each location within the 7201 array i get the following probability plots.
probability 1-2.jpg is the p-value computed from the t-test when comparing data1 to data2.
probability 1-3.jpg is the p-value computed from the t-test when comparing data1 to data3.
probability 2-3.jpg is the p-value computed from the t-test when comparing data2 to data3.
The result of the t-test shows that they are "at times" statistically the same however at other times not equivalent. I can understand this for (x-values) of -360 to ~0 although from the (x-values) 0 to 100 they should be statistically the same according to the standard deviation plots. Is this conclusion not what I should expect?
Perhaps someone with more experience in t-test may be able to help determine which conclusions I can draw from this data. To me it simply doesn't make any sense.
********************************************************************
SEE THE FITH POST OF THIS THREAD TO GET A BETTER IDEA OF THE ISSUE
********************************************************************
I am comparing three datasets of length 7201 datapoints each (on images this is the x-axis values). At every one of the 7201 datapoints I have 989, 1003, 945 trials each (on the figures this is the z-axis values). think of it as a matrix, 7201 x ~1000 for each dataset. I take the means and standard deviations in the '1000' direction to provide a 7201 x 1 array for the mean (then do the same for the Std.Dev). Now when I plot this data they overlap according to the following figure (data-means.jpg), the top hump is the mean+1\sigma, the bottom hump is the mean-1\sigma, and the middle hump is the mean of each dataset.
So graphically I can conclude that the three datasets are the same, however when I conduct the two tailed t-test (as well as the F-test of variance) comparing each location within the 7201 array i get the following probability plots.
probability 1-2.jpg is the p-value computed from the t-test when comparing data1 to data2.
probability 1-3.jpg is the p-value computed from the t-test when comparing data1 to data3.
probability 2-3.jpg is the p-value computed from the t-test when comparing data2 to data3.
The result of the t-test shows that they are "at times" statistically the same however at other times not equivalent. I can understand this for (x-values) of -360 to ~0 although from the (x-values) 0 to 100 they should be statistically the same according to the standard deviation plots. Is this conclusion not what I should expect?
Perhaps someone with more experience in t-test may be able to help determine which conclusions I can draw from this data. To me it simply doesn't make any sense.
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