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Sample size calculations are now mandatory for many research protocols, but the ones useful in common situations are not all easily accessible. This paper outlines the ways ofcalculating sample sizes in two group studies for binary, ordered categorical, and continuous outcomes. Formulas and worked examples are given. Maximum power is usually achieved by having equal numbers in the two groups. However, this is not always possible and calculations for unequal group sizes are given.A sample size calculation is now almost mandatory in research protocols and to justify the size of clinical trials in papers.' Nevertheless, one of the most common faults in papers reporting clinical trials is in fact a lack of justification of the sample size, and it is a major concern that important therapeutic effects are being missed because of inadequately sized studies.2 A recent paper has concluded "the reporting of statistical power and sample size needs to be improved."3 Recent articles in the BMJ have described the basis of sample size calculations,4 5 and explained the fundamental concepts of statistical significance (oa), effect size (8) Of all the parameters that have to be specified before the sample size can be determined the most critical is the effect size. Reducing the effect size by half will quadruple the required sample size. The effect size can be interpreted as a "clinically important difference," but this is often difficult to quantify. A valuable attempt at classification was made by Burnand et al, who reviewed three major medical journals and looked for words such as "impressive difference," "important difference," "dramatic increase" and then calculated a standardised effect size.8 This provided a guide to the size of effect regarded as important by other authors. There are several ways of eliciting useful sample sizes: a Bayesian perspective has been given recently,9 along with an economic approach,'0 and one based on patients' rather than clinicians' perceptions of benefit."In statistical significance tests one sets up a null hypothesis and, given the observed difference of interest, calculates the probability of observing the difference (or a more extreme one) under the null hypothesis. This yields the P value. If the P value is less than some prespecified level then we reject the null hypothesis. This level is known as the significance level oa. If we reject the null hypothesis when it is true we make a type I error, and we set oa, the significance level, to control the probability of doing this. If the null hypothesis is in fact false but we fail to reject it, we make a type II error, and the probability of a type II error is denoted as P. The probability of rejecting the null hypothesis when it is false is termed the power and is defined as 1-[.

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We describe new charts and tables for dating pregnancies derived from data collected in a study designed to enable the construction of these and charts of fetal size. This was a prospective study in which 663 fetuses seen in the routine ultrasound clinic in a London teaching hospital were scanned once only for the purpose of this study. We discuss the statistical methodology in detail and compare the new charts with other published charts. We suggest that the differences seen may be due to variation in study methodology.