Also, what would a negative t-statistic imply?

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Also, what would a negative t-statistic imply?

The t-test uses something called the t-distribution to work out if sample differences are significant. (see http://www.statistics4u.com/fundstat_eng/cc_distri_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

The t-test uses something called the t-distribution to work out if sample differences are significant. (see http://www.statistics4u.com/fundstat_eng/cc_distri_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

1) You do not get the probability of the difference being random. And you do not get the probability of a certain t-value (you may not mean these things but you seem to invite that interpretation). The p-value just tells you the probability of observing a difference as extreme as the observed one

2) t can be negative (depending on which mean is subtracted from which), but p cannot of course.

That´s all