# Why does t statistic increase with the sample size?

I have a question on reading t-test p-value. If I understand correctly, the t-statistic is computed as:

$t = \frac{\bar{X} - \mu}{\sigma / \sqrt{n}}$

where $\bar{X}$ is sample mean, $\mu$ is population mean, $\sigma$ is sample standard deviation and $n$ is size of sample. Degrees of freedom is $n-1$.

Now, we can see that the t-statistic is inversely proportional to the standard error/variance of the sample population ($\sigma / \sqrt{n}$). Higher $n$ leads to smaller standard error that gives higher t-value. Higher t-value means lower p-value infering that the difference between sample-mean ($\bar{X}$) and population-mean ($\mu$) is significant (hence we reject the null hypothesis).

But this formula seems counter-intuitive to me as bigger sample size (higher $n$) should give sample mean closer to population mean.

How do we explain this?

-
 In what sense do you mean "increase with sample size?" After all, $\bar{X}$ changes with sample size, too: you can't hold it fixed while increasing $n$. To put it another way: $t$ is a random variable. Therefore, for any fixed population, you obtain a sequence of random variables, one for each $n$. Could you clarify what you mean for this sequence of random variables to be "increasing"? – whuber♦ Jul 31 '11 at 19:49

A little change in notation might help in answering your question: What you call $\mu$ is often called $\mu_{0}$ because it is the population mean under the null hypothesis, whereas $\mu$ is the actual population mean - which is unknown because we don't know whether the null hypothesis is true. Also, what you call $\sigma$ is usually called $s$ following the convention that population parameters get greek letters, and sample parameters are denoted by latin letters.

Note that $s / \sqrt{n}$ is the standard error of the mean $\bar{X}$ (SEM) - an estimate of the variablity of $\bar{X}$, where $\bar{X}$ is understood as a random variable. So we have

$t = \frac{\bar{X} - \mu_{0}}{s / \sqrt{n}}$

Now, for a given sample, you have a fixed empirical $\bar{X}_{emp}$, and thus a fixed difference $d_{emp} = \bar{X}_{emp} - \mu_{0}$. Part of the confusion seems to be related to the idea that "a bigger sample size (higher $n$) should give sample mean closer to population mean". This should be rephrased to conditional on the null hypothesis being true ($\mu = \mu_{0}$), the probability of observing a difference $d = \bar{X} - \mu_{0}$ that is at least as large as the already observed $d_{emp}$ becomes smaller when $n$ increases. This is because the "accuracy" of our estimator $\bar{X}$ then increases (variability decreases).

I guess the main point is that you already have a fixed $\bar{X}_{emp}$ and thus $d_{emp}$, and $t$ just tells you how "big that difference is" measured in (estimated) units of variability of $\bar{X}$. When the units become smaller in absolute numbers, the same absolute difference $d_{emp}$ will "be worth more units" and will thus count as "more suprisingly high" (= less likely to occur) if $\mu = \mu_{0}$.

-
Thanks for the sound explanation. I was confusing myself with samples selection but now it is clear. – user862 Aug 6 '11 at 11:22

Someone else can probably give a more rigorous answer, but:

For any given (fixed) difference between $\bar{X}$ and $\mu$, the difference is more meaningful if n is high.

Increasing n will result in a sample mean closer to the population mean, but only in the case that your sample is not different from the population. So when n is high and $\bar{X}$ still differs from $\mu$, that reinforces the rejection of the null hypothesis.

-
That's an interesting explanation. However, I still don't understand when you have two samples of different sizes with the same difference in mean from population mean, why do we penalize the larger sample? – user862 Jul 31 '11 at 18:12
Hmm - I think you actually 'reward' the one with bigger n, because $t= \frac{\mu-\mu_0}{S/{\sqrt{N}}} = \frac{\sqrt{N}(\mu-\mu_0)}{S}$ Yah? – ImAlsoGreg Jul 31 '11 at 22:19

Your last sentence seems to capsulize the confusion.

You wrote

But this formula seems counter-intuitive to me as bigger sample size (higher n) should give sample mean closer to population mean.

but this is true only if the sample is from a population that has the same mean as the population it is being compared to. The word "population" is being used to refer to two different populations

-