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Statistical Methods in Medical Research 4ed

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110 Analys<strong>in</strong>g means and proportions<br />

However, this method is no more exact for f<strong>in</strong>ite values of n1 and n2 than<br />

would be the use of the normal approximation to the t distribution <strong>in</strong> the case of<br />

equal variances. The appropriate analogue of the t distribution is both more<br />

complexthan the t distribution and more contentious. One solution, due to<br />

B.L. Welch, is to use a distribution for d (tabulated, for example, <strong>in</strong> Pearson and<br />

Hartley, 1966, Table 11). The critical value for any particular probability level<br />

depends on s2 1 =s2 2 , n1 and n2. Another solution, similarly dependent on s2 1 =s2 2 , n1<br />

and n2, is that of W.V. Behrens, tabulated as Table VI <strong>in</strong> Fisher and Yates<br />

(1963). The dist<strong>in</strong>ction between these two approaches is due to different<br />

approaches to the logic of statistical <strong>in</strong>ference. Underly<strong>in</strong>g Welch's test is an<br />

<strong>in</strong>terpretation of probability levels, either <strong>in</strong> significance tests or confidence<br />

<strong>in</strong>tervals, as long-term frequencies <strong>in</strong> repeated samples from the same<br />

populations. The Behrens test was advocated by R.A. Fisher as an example of<br />

the use of fiducial <strong>in</strong>ference, and it arises also from the Bayesian approach (§6.2).<br />

A feature of Welch's approach is that the critical value for d may be less than<br />

the critical value for a t distribution with n1 ‡ n2 2 DF, and this is unsatisfactory.<br />

A simpler approximate solution which does not have this disadvantage is to<br />

test d aga<strong>in</strong>st the t distribution with degrees of freedom, n, dependent on s2 1 =s2 2 , n1<br />

and n2 accord<strong>in</strong>g to the follow<strong>in</strong>g formula (Satterthwaite, 1946):<br />

…s2 1 =n1 ‡ s2 2<br />

2 =n2†<br />

n<br />

ˆ …s2 2<br />

1 =n1†<br />

=n2† 2<br />

n1 1 ‡ …s22 : …4:11†<br />

n2 1<br />

Although this test uses the t distribution, it should not be confused with the more<br />

usual two-sample t test based on equal variances. This approximate test is<br />

<strong>in</strong>cluded <strong>in</strong> some statistical software packages.<br />

Example 4.5<br />

A suspension of virus particles is prepared at two dilutions. If the experimental techniques<br />

are perfect, preparation B should have 10 times as high a concentration of virus particles<br />

as preparation A. Equal volumes from each suspension are <strong>in</strong>oculated on to the chorioallantoic<br />

membrane of chick embryos. After an appropriate <strong>in</strong>cubation period the<br />

membranes are removed and the number of pocks on each membrane is counted. The<br />

numbers are as follows:<br />

Preparation<br />

Counts<br />

A B<br />

0 10<br />

0 13<br />

1 13<br />

1 14<br />

1 19<br />

1 20<br />

2 21<br />

2 26<br />

3 29

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