Over the past 10 years or so, confidence intervals have become increasingly recognised in program evaluation and quantitative health measurement generally as the preferred way of reporting the accuracy of statistical estimates. Statisticians have found that the more traditional ways of reporting results - using P-values and hypothesis tests - are often very difficult to interpret and can be misleading. This is particularly the case when sample sizes are small and results are 'negative' (ie P>0.05); in these cases, a confidence interval can communicate much more information about the sample and, by inference, about the population. Despite this trend among statisticians and health promotion evaluators towards the use of confidence intervals, it is surprisingly difficult to find succinct and reasonably simple methods to actually compute a confidence interval. This is particularly the case for proportions or percentages. Much of the data which are analysed in health promotion are binary or categorical, rather than the quantities and continuous variables often found in laboratories or other branches of science, so there is a need for health promotion evaluators to be able to present confidence intervals for percentages or proportions. However, the most popular statistical analysis computer package among health promotion professionals, SPSS does not have a routine to compute a simple confidence interval for a proportion! To address this shortcoming, I present in this paper some fairly simple strategies for computing confidence intervals for population percentages, both manually and using the right computer software.