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Hi all,

I am stumped on how to achieve this with the data I have. My PI thinks there is a way, but I can't seem to find it. We want to see whether we should go ahead with this experiment or if it will take too much time.

Basically, I have 2 sets of data. I am looking to see which one conforms to a logarithmic regression line better, i.e. which one has better fit. To do that, I checked the r^2 values, and then did a t-test for unequal variance on the residual sum of squares. The difference was miniscule (p=.73).

Now, what I have at the moment is the formula for the t-test for unequal variances:

t=(xa-xb)/sqrt((sa+sb)/n)

where xa and xb are the sample means, sa and sb are the sample variances, and n is the number of trials (the actual formula uses sa/na + sb/nb, but I have the same number of data points for each set of data)

I manipulated it to show how many trials I need until t=1.96 (basically just close to significance)

n=(sa+sb)/((xa-xb)/t)^2

The issue I have with this, though, is that the means and variances could be different. How can I use this to calculate the estimated number of trials when the data I have may not be accurate? Is there even a way to do this?

Thanks for any help.

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# Using t-tests to get trials until significance ?

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