# 17.3.2.2 Algorithms (Two-Sample T-Test)

## Contents

The two sample t-test calculates a Student's t statistic and the associated probability to test whether or not the difference of the two sample means equals to $\mu_d\,\!$ (i.e. to test whether or not their means are equal, you can just test whether or not their difference is 0, $\mu_1-\mu_2=\mu_d=0\,\!$ ). And the hypotheses take the form: $H_0:\mu_1-\mu_2=\mu_d\,\!$ vs $H_1:\mu_1-\mu_2 \ne \mu_d$ Two Tailed $H_0:\mu_1-\mu_2 \le \mu_d$ vs $H_1:\mu_1-\mu_2 > \mu_d$ Upper Tailed $H_0:\mu_1-\mu_2 \ge \mu_d$ vs $H_1:\mu_1-\mu_2 < \mu_d$ Lower Tailed

### Test Statistics

Consider two independent samples $x_1\,\!$and $x_2\,\!$, of size $n_1\,\!$ and $n_2\,\!$ drawn from two normal population with means $\mu_1\,\!$ and $\mu_2\,\!$, and variances $\sigma_1^2\,\!$ and $\sigma_2^2\,\!$ respectively, we have: $\bar{x}_1=\frac{1}{n_1}\sum_{j=1}^{n_1}x_{1j}$, $\bar{x}_2=\frac{1}{n_2}\sum_{j=1}^{n_2}x_{2j}$, $s_1^2=\frac{1}{n_1-1}\sum_{j=1}^{n_1}{(x_{1j}-\bar{x}_1)^2}$, $s_2^2=\frac{1}{n_2-1}\sum_{j=1}^{n_2}{(x_{2j}-\bar{x}_2)^2}$

where $\bar{x}_1\,\!$and $\bar{x}_2\,\!$ are sample means and $s_1^2\,\!$ and $s_2^2\,\!$ are sample variances. Then we can compute the t test statistic by:

For equal variance is assumed, that is $\sigma_1^2=\sigma_2^2\,\!$:

In this case the test statistic t: $t=\frac{(\bar{x}_1-\bar{x}_2)-\mu_d}{s_p\sqrt{(1/n_1+1/n_2)}}$

has a t-distribution with $(v = n_1+n_2-2)$ degrees of freedom and $s_p=\sqrt{\frac{(n_1-1)s_1^2+(n_2-1)s_2^2}{n_1+n_2-2}}$

is the pooled variance of the two samples.

For equal variance is not assumed:

In this case the usual two sample t-statistic no longer has a t-distribution and an approximate test statistic, t'is used: $t'=\frac{(\bar{x}_1-\bar{x}_2)-\mu_d}{\sqrt{\frac{s_1^2}{n_1}+\frac{s_2^2}{n_2}}}$

And a t-distribution with v degrees of freedom is used to approximate the distribution of t'where $v=\frac{(s_1^2/n_1+s_2^2/n_2)^2}{\frac{(s_1^2/n_1)^2}{n_1-1}+\frac{(s_2^2/n_2)^2}{n_2-1}}$

Then compare the t value with the critical value and we will reject $H_0\,\!$ if:

Two tailed test: $|t| > t_{\sigma/2}\,\!$;

Upper tailed test: $t > t_\sigma\,\!$;

Lower tailed test: $t < -t_\sigma\,\!$;

The p-value will also be compared with a user-defined significance level, $\sigma\,\!$, which commonly 0.05 is used. And the null hypothesis $H_0\,\!$ will be rejected if $p < \mu\,\!$.

### Confidence Intervals

The upper and lower $(1-\sigma )\times 100\%$ confident limits for mean difference $(\mu_1 - \mu_2)\,\!$ are calculated as:

For equal variance is assumed:

Null Hypothesis Confidence Interval $H_0:\mu_1-\mu_2=\mu_d\,\!$ $\left[(\bar{x}_1-\bar{x}_2)- t_{\alpha/2}s_p\sqrt{\frac{1}{n_1}+\frac{1}{n_2}}, (\bar{x}_1-\bar{x}_2)+ t_{\alpha/2}s_p\sqrt{\frac{1}{n_1}+\frac{1}{n_2}}\right]$ $H_0:\mu_1-\mu_2 \le \mu_d$ $\left[(\bar{x}_1-\bar{x}_2)- t_{\alpha}s_p\sqrt{\frac{1}{n_1}+\frac{1}{n_2}}, \infty\right]$ $H_0:\mu_1-\mu_2 \ge \mu_d$ $\left[-\infty, (\bar{x}_1-\bar{x}_2)+ t_{\alpha}s_p\sqrt{\frac{1}{n_1}+\frac{1}{n_2}}\right]$

For equal variance is not assumed:

Null Hypothesis Confidence Interval $H_0:\mu_1-\mu_2=\mu_d\,\!$ $\left[(\bar{x}_1-\bar{x}_2)- t_{\alpha/2}\sqrt{\frac{s_1^2}{n_1}+\frac{s_2^2}{n_2}}, (\bar{x}_1-\bar{x}_2)+ t_{\alpha/2}\sqrt{\frac{s_1^2}{n_1}+\frac{s_2^2}{n_2}}\right]$ $H_0:\mu_1-\mu_2 \le \mu_d$ $\left[(\bar{x}_1-\bar{x}_2)- t_{\alpha}\sqrt{\frac{s_1^2}{n_1}+\frac{s_2^2}{n_2}}, \infty\right]$ $H_0:\mu_1-\mu_2 \ge \mu_d$ $\left[-\infty, (\bar{x}_1-\bar{x}_2)+ t_{\alpha}\sqrt{\frac{s_1^2}{n_1}+\frac{s_2^2}{n_2}}\right]$

where $t_{\sigma/2}\,\!$ is the critical value of the t-distribution with v degrees of freedom.

### Power Analysis

The power of a two sample t-test is a measurement of its sensitivity. Detail algorithm about calculating power please read the help of Power and Sample Size.

### Reference

The two-sample t-test is implemented with a Nag function, nag_2_sample_t_test (g07cac). Please refer to the corresponding Nag document for more details on the algorithm.