# Different notations for cutoff values in statistical tests ($z_{\frac{\alpha}{2}}$ and $t_{\frac{\alpha}{2}}$)

I realize that this question is most likely not too interesting and challenging to discuss here, it's rather a matter of different notations, but I would like to clarify that if possible.

When conducting Z/t tests, I got used to the convention that $$z_{\frac{\alpha}{2}}$$ is such a value that $$P(Z>z_\frac{\alpha}{2}) = \frac{\alpha}{2}$$. It also follows that $$-z_{\frac{\alpha}{2}} = z_{1-\frac{\alpha}{2}}$$. The same is true for the t-test and is just a matter of swapping the letters.

Visually speaking, this is just such a value that the area to the right equals $$\frac{\alpha}{2}$$.

I noticed, however, that sometimes these cutoff values are introduced as the inverse CDFs and then the order of the values on the X-axis is reversed, of course.

What is interesting, some authors mix the two and then their notation gets a bit confusing. See, for example, below:

https://online.stat.psu.edu/stat500/lesson/6b/6b.1

$$t_{0.005}$$ is positive here in the solution, but the illustrations place $$t_{1-\alpha}$$ on the right of the X-axis, which doesn't make a lot of sense to me unless there is this notation failure.

What is the more widely accepted notation? Well, I understand that this is not a big deal at all and it's always clear from context which notation the author prefers, but I just stumbled upon the online book above and this inconsistency made me wonder.

Thanks!

• I get the feeling that half of statisticians think each way is right and think the other half is fools for believing otherwise. – Dave Mar 22 at 14:12
• It just surprises me that there can be a second opinion on a thing like this one. In the book above, this duality only resulted in somewhat confusing illustrations. Honestly speaking, currently they are just wrong – Don Draper Mar 22 at 14:14
• This is indeed a big problem in statistics, especially for students. I am a strong proponent of a consistent notation for quantiles of the (continuous) distribution of $X$: $q_{\alpha}$ is always the value so that $\alpha = \text{Prob}[X\leqslant q_{\alpha}]$. Or for other distributions: $q_{\alpha}=F^{-1}(\alpha)$ for some suitably defined inverse of the cumulative distribution function $F(x)$ of $X$. – StijnDeVuyst Mar 22 at 15:45

For example, in R, code qt(.975, 24) returns $$2.063899,$$ called 'quantile 0.975'. You can probably find something like $$2.064$$ on line 24 of your t table under header $$0.025$$ (sometimes called a 'percentage point').
I am not seriously proposing this, but maybe it should be something like $$t_{0.025} = t_{24; 0.025} \approx 2.064$$ and $${}_{0.975}t = {}_{0.975}t_{24} = 2.063899.$$