Hi land144,

The t-test uses something called the t-distribution to work out if sample differences are significant. (see

http://www.statistics4u.com/fundstat...student_t.html)

It is basically a probability density function. The higher the absolute value of the t-statistic, the lower the probability - that is the lower the probability that the difference is random, i.e more probable that the difference observed is due to a systematic influence (i.e. experimental intervention). The actual distribution depends on the degrees of freedom, or in this case the number of observations-1. Using the criteria of alpha=0.05, one can work out what the critical t-value should be for the degrees of freedom.

Different statistical tests use different probablity functions, e.g. ANOVA uses the F distribution.

Assuming infinite degrees of freedom: Using random data to get a t-value of 0.94 is expected, as it would not be signficant - after all, it was randomly selected. Getting a t-value of 2.5 would be signficant, as alpha would be <0.05. Have a look at this table

http://www.socr.ucla.edu/Applets.dir/T-table.html and see what you get with the df in your case.

You can't have a negative t-statistic because you can't have a 'negative probability' or something happening or not happening.

Hope this helps

William