GraphPad Statistics Guide

Results from test for trend

Results from test for trend

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Results from test for trend

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Interpreting the results

Slope

This is the change in the mean value per column as you move one column to the right. In other words, it assumes that the X values corresponding to column order are separated by 1 (we call this the span). Note that Prism 6 and earlier used a span of 1 when there were an odd number of columns, but a span of 2 when there were an even number of columns (this was not made clear in the results). With an even number of columns, therefore, the slope reported by Prism 6 (or earlier) is twice the slope that Prism now reports.

R square

Prism reports two different R2 in the context of testing for linear trend after ANOVA.  

The effect size R2  is the fraction of the total variance accounted for by the linear trend.This was the only R2 reported by Prism 6 which labeled it simply R2.

The alerting R2 is the fraction of the variance between group means that is accounted for by the linear trend.  Because the variance between group means is always less than the total variance, the alerting R2 is always higher than the effect size R2.

P values

Prism reports two P values.

Test for linear trend. The P value tests the null hypothesis that there is no linear trend between the population means and group order. It answers the question: If there really is no linear trend between column number and column mean, what is the chance that random sampling would result in a slope as far from zero (or further) than you obtained here? If the P value is small, conclude that there is a statistically significant linear trend. As you go from left to right in the data table, the column means tend to get higher (or lower).

Test for nonlinear trend. If you have four or more groups (data set columns), then Prism also reports a P value testing nonlinear trend. The null hypothesis is that the entire relationship between the column means and column order is linear. A small P value tells you there is also a nonlinear trend.