**Re: Correlation using t-test and correlation coefficient - What is the correct result**
Hello guys,

Its been a while since I wrote in this forum but I've been reading a lot on those threads.

Well I need the experts help here.

I usually does not encounter this problem but time and again this issue comes up. I have 2 sets of data, same sample measurement, the sample size is 20. Here are the details :

Mean 1=0.1146 Mean2 =0.1185

SD 1=0.011038 SD2 = 0.0636

t-test for dependent sample result showed, p>0.05 which suggest the 2 sets are not significantly different which I can not accept.

Is this result distorted because of the SD problem ? I remember that an f-test should be performed to test equality of variances, do you think that that is the reason for this?

Therefore for this case I ignored the result of the t-test and referred to the linear regression coefficient of r as basis for the correlation.

Need the expert's input if the approach is correct.

Thanks,

Jefnik

The t-test is used for comparing two samples and checking if they came from the same population. No time sequence is maintained, each of the two samples are collapsed into their descriptive statistics.

However, linear regression is used for checking if there is a linear relationship between two variables. I assume when you did the linear regression, your x-axis was time.

The difference between the two tests is significant - the t-test samples may not even exist in two distinct time sequences, they may be intermingled (such as on days 1, 5, 7, 8, and 10 one method was used, and on the other days a different method was used). Also, even if the results are in sequence (the first 20 days are the first sample, the second 20 days the second sample), the t-test may show the two sets are different, but there may not be a linear relationship, giving a different result on the linear regression.

Personally, I'd use control charts if you have data in a time sequence.