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The chi-square test was then used to determine if the distribution of scores between those sets was statistically significant.
The results provided no reason against the initial hypothesis arguing that the distribution of statistical differences for each measured pair of data sets is statistically different from the average in each of the studied groups.
A statistical analysis was performed to determine if differences between reduction factors for the two sets were statistically significant.
As you know, the t-test assesses whether the means of two data sets are statistically different from each other.
As a rule, differences between the mean intensities for these two data sets were statistically insignificant (t-test; P < 0.05).
However, the difference between the two peptides sets is statistically significant (p<0.05, paired T test).
To determine whether differences between data sets were statistically significant, Student's t-test analysis (two-tailed distribution; two-sample unequal variance) was performed using Excel software.
All of these data sets are statistically heterogeneous.
All three of these data sets were statistically heterogeneous.
The control "sets" were statistically analyzed comparing the data to each other before being combined.
Regression equations and slopes between the two monitoring data sets were statistically indistinguishable.
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