## From last time

**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 \(\mu_Y - \mu_X\)

Properties of sampling distribution for \(\overline{Y} - \overline{X}\), lead to Z-test and associated intervals:

\[ Z(\delta_0) = \frac{(\overline{Y} - \overline{X}) - \delta_0}{\sqrt{\sigma_Y^2/n + \sigma^2_X/m}} \]

With known population variances \(\sigma_Y^2\), \(\sigma_X^2\).

## When variances aren’t known

Like in one-sample Z-test, we proceed by substituting in good estimates for the variances, then alter reference distibutions accordingly.

Two scenarios:

Populations variances are unknown but assumed equal, \(\sigma^2 = \sigma_Y^2 = \sigma_X^2\). Both samples give information about \(\sigma^2\).

Populations variances are unknown and not assumed equal.

## Equal variances

Need to use both samples to estimate \(\sigma^2 = \sigma_Y^2 = \sigma_X^2\)

\[ \begin{aligned} s_p^2 = \hat{\sigma}^2 &= \frac{\sum_{i = 1}^n \left(Y_i - \overline{Y} \right)^2+ \sum_{i = 1}^m \left( X_i - \overline{X} \right)^2}{(n - 1) + (m - 1)} \\ &= \frac{(n-1)s_Y^2 + (m-1)s_X^2}{n + m - 2} \end{aligned} \]

where \(s_Y^2\) and \(s_X^2\) are the samples variances for the \(Y_i\) and \(X_i\) respectively.

Intuition: weighted average of sample variances, so that larger sample should contribute more in the average.

## Plugging in to Z-stat

Hypothesis: \(H_0: \mu_Y - \mu_X = \delta_0\)

Assumption: \(\sigma_Y^2 = \sigma_X^2\)

Leads to test statistic: \[ t(\delta_0) = \frac{(\overline{Y} - \overline{X}) - \delta_0}{\sqrt{s_p^2/n + s_p^2/m}} = \frac{(\overline{Y} - \overline{X}) - \delta_0}{\sqrt{s_p^2 \left(\frac{1}{n} + \frac{1}{m}\right)}} = \frac{(\overline{Y} - \overline{X}) - \delta_0}{s_p\sqrt{ \left(\frac{1}{n} + \frac{1}{m}\right)}} \]

## Leads to equal variance t-test

Compare \(t(\delta_0\))$ to a t-distribution with \(n+m-2\) degrees of freedom.

Also leads to CI of form:

\[ (\overline{Y} - \overline{X}) \pm t_{(n+m-2), 1-\alpha/2} \sqrt{s_p^2 \left(\frac{1}{n} + \frac{1}{m}\right)} \]

This distribution is **exact** if the populations are Normal.

Assymptotically exact otherwise.

For large sample sizes, it doesn’t make much difference \(t_{m+n-2} \rightarrow z\) as \(n+m-2 \rightarrow \infty\)

## Equal variance assumption: What can go wrong?

Compare \(E(s_p^2/n + s_p^2/m)\) to \(Var(\overline{Y} - \overline{X})\)

## Equal variance assumption: What can go wrong?

Actual = \(Var(\overline{Y} - \overline{X}) = \frac{\sigma_Y^2}{n} + \frac{\sigma_X^2}{m}\)

Estimated = \(E(\widehat{Var}(\overline{Y} - \overline{X})) \approx \frac{\sigma_Y^2}{m} + \frac{\sigma_X^2}{n}\)

m | \(\sigma_X^2\) | n | \(\sigma_Y^2\) | Actual | Estimated |
---|---|---|---|---|---|

10 | 1 | 50 | 4 | 0.18 | 0.42 |

10 | 9 | 50 | 1 | 0.92 | 0.28 |

## Equal variance assumption: Consequences

The expected value of the estimated variance is:

Larger than it should be when the smaller sample comes from the population with the smaller variance.

- Test statistic will be closer to zero than it should be, and rejection rates will be smaller.

Smaller than it should be when the smaller sample comes from the population with the larger variance.

- Test statistic will have a larger absolute value than it should, and rejection rates will be larger.

## If we don’t assume equal variance?

What’s the best estimate of \(\frac{\sigma_Y^2}{n} + \frac{\sigma_X^2}{m}\)?

\[ \frac{s_Y^2}{n} + \frac{s_X^2}{m} \]

Plugging into Z-stat:

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

Reference distribution? Even when populations are Normal, this test statistic doesn’t have exactly a t-distribution.

## Welch-Satterthwaite

Slightly better than just using a Normal approximation.

Compare to \(t\) with \(v\) degrees of freedom, where \[ v = \frac{(s_Y^2/n + s_X^2/m)^2}{\frac{s_Y^4}{n^2(n-1)} + \frac{s_X^4}{m^2(m-1)} } \] Somewhere between \(\min(m-1, n-1)\) and \(m+n-2\)