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# Review of last week’s t-tests

## Setting

Setting: two independent samples

$$Y_1, \ldots, Y_n$$ i.i.d from population with c.d.f $$F_Y$$, and
$$X_1, \ldots, X_m$$ i.i.d from population with c.d.f $$F_X$$

Parameter: Difference in population means $$\mu_Y - \mu_X$$

## Equal variance two sample t-test

Assume $$\sigma_X^2 = \sigma_Y^2$$.

$t(\delta_0) = \frac{(\overline{Y} - \overline{X}) - \delta_0}{\sqrt{s_p^2 \left(\frac{1}{n} + \frac{1}{m}\right)}}$

Compare to $$t_{(n+m-2)}$$.

## Welch’s t-test

$$\sigma_X^2$$ not necessarily equal to $$\sigma_Y^2$$.

$t(\delta_0) = \frac{(\overline{Y} - \overline{X}) - \delta_0}{\sqrt{\frac{s_Y^2}{n} + \frac{s^2_X}{m}}}$

Compare to $$t_{v}$$, where

$v = \frac{(s_Y^2/n + s_X^2/m)^2}{\frac{s_Y^4}{n^2(n-1)} + \frac{s_X^4}{m^2(m-1)} }$

## In both cases

If $$\textit{df}$$ is the appropriate degrees of freedom for the test.

Rejection regions:

• $$H_A: \mu_Y - \mu_X > 0$$: Reject $$H_0$$ for $$t(\delta_0) > t_{(df), 1 - \alpha}$$
• $$H_A: \mu_Y - \mu_X < 0$$: Reject $$H_0$$ for $$t(\delta_0) < t_{(df), \alpha}$$
• $$H_A: \mu_Y - \mu_X \ne 0$$: Reject $$H_0$$ for $$|t(\delta_0)| > t_{(df), 1 - \alpha/2}$$

Confidence intervals:

$\overline{Y} - \overline{X} \pm t_{(df), 1-\alpha/2} \text{SE}_{\overline{Y} - \overline{X}}$

# Paired Data

## Setting

Two dependent samples

$$Y_1, \ldots, Y_n$$ i.i.d from population with c.d.f $$F_Y$$, and
$$X_1, \ldots, X_n$$ i.i.d from population with c.d.f $$F_X$$

Observations come in pairs: $(Y_1, X_1), (Y_2, X_2), \ldots, (Y_n, X_n)$

with joint distribution $$F_{YX}$$. Observations are somehow matched.

$$Cov(Y_i, X_i) = \sigma_{YX}$$ and $$Cov(Y_i, X_j) = 0$$ for all $$i \ne j$$.

Parameter: Difference in population means $$\mu_Y - \mu_X$$

## Examples

We’ve already seen examples like this:

• midterm Mother’s IQ ($$Y_i$$) and Father’s IQ ($$X_i$$)
• homework Current weight ($$Y_i$$) and desired weight ($$X_i$$)

We did one sample t-tests on the differences $$Y_i - X_i$$. This works, but why?

## Sampling Distirbution of difference in sample means

Consider $D_i = Y_i - X_i$

CLT says $\frac{\overline{D} - E(D_i)}{\sqrt{Var(D_i)/n}} \, \dot \sim \, N(0, 1)$

What are $$\overline{D}$$, $$E(D_i)$$ and $$Var(D_i)$$?

## A Z-test for paired data

If $$\sigma^2_Y$$, $$\sigma^2_X$$, and $$\sigma_{YX}$$ are known.

Hypothesis Test for $$H_0: \delta = \delta_0$$

Test Statistic: $Z(\delta_0) = \frac{(\overline{Y} - \overline{X}) - \delta_0}{\sqrt{\frac{\sigma^2_Y}{n} + \frac{\sigma^2_X}{n} - 2 \frac{\sigma_{YX}}{n}}}$

Reference Distribution: Under $$H_0$$, $$Z(\delta_0) \dot \sim N(0, 1)$$

Rejection Region:

• $$H_A: \delta > \delta_0$$: Reject $$H_0$$ for $$z(\delta_0) > z_{1-\alpha}$$
• $$H_A: \delta < \delta_0$$: Reject $$H_0$$ for $$z(\delta_0) < z_{\alpha}$$
• $$H_A: \delta \ne \delta_0$$: Reject $$H_0$$ for $$|z(\delta_0)| > z_{1-\alpha/2}$$

## A Z-test for paired data

$Z(\delta_0) = \frac{(\overline{Y} - \overline{X}) - \delta_0}{\sqrt{\frac{\sigma^2_Y}{n} + \frac{\sigma^2_X}{n} - 2 \frac{\sigma_{YX}}{n}}}$

Notice the test statistic is just like a two sample Z-test, but with a correction to $$Var(\overline{Y} - \overline{X})$$ for the correlation between $$Y_i$$ and $$X_i$$.

## What if population variances and covariances aren’t known?

Plug in estimates for $$\sigma_Y^2$$, $$\sigma_X^2$$ and $$\sigma_YX$$.

Sample covariance: $\hat{\sigma}_{YX} = s_{YX} = \frac{1}{n-1} \sum_{i=1}^{n} (Y_i - \overline{Y}) (X_i - \overline{X})$ is an unbiased estimate of $$\sigma_{YX}$$.

Plugging in the estimates gives the estimated variance of $$\overline{Y} - \overline{X}$$: $\widehat{Var}\left(\overline{D}\right) = \frac{s^2_Y}{n} + \frac{s^2_X}{n} - 2 \frac{s_{YX}}{n}$

## Compare to estimated $$Var(D_i)$$

$$s_D^2 =$$

## Paired data t-test

$t(\delta_0) = \frac{(\overline{Y} - \overline{X}) - \delta_0}{\sqrt{\frac{s^2_Y}{n} + \frac{s^2_X}{n} - 2 \frac{s_{YX}}{n}}} = \frac{\overline{D}- \delta_0}{\sqrt{\frac{s_D^2}{n}}}$

If differences are Normal, $$t(\delta_0)$$ has exactly a t-distribution with $$n-1$$ degrees of freedom when the null hypothesis is true.

## Summary

For paired samples:

1. Take differences $$D_i = X_i - Y_i$$
2. Perform a one-sample hypothesis test for the population mean difference $$\mu_D = \mu_Y - \mu_X$$

That is, do a one-sample t-test on the differences

This is equivalent to estimating the population covariance and appropriately adjusting the denominator of the two-sample t-test to take this covariance into account