In vision sciences it is common to have study designs with many within subject conditions. The data are often presented in bar graphs with standard error bars. Non-overlapping standard error bars do not necessarily mean statistically significant difference. Similarly, overlapping 95% confidence intervals do not necessarily mean lack of a significant difference. We reviewed the literature that suggests a confidence interval can be derived that allows comparison between all means on a single chart. We then provide a simple graphical method in Excel that uses stacked bar graphs to create 84% confidence intervals in which non-overlapping bars are significant at an unadjusted p<.05. The bars can also be constructed using a Bonferonni adjustment considering (n-1)! comparisons. As an example we provide the results of a reaction time detection task using PowerPoint slides with 8 emphasis conditions (Bold, Italic, Underline, CAPS, red, yellow, green, blue) on 3 backgrounds (white, black, dark blue). We propose a method of estimating the standard error from the output of a maximum likelihood mixed model analysis of variance (SPSS 17, IBM Corp). Specifically we perform a one way analysis of variance and use the largest standard error of the differences (SED) from the paired comparisons output. The SEM is estimated as the square root of SED2/2. SEDs vary across comparisons if there is missing data, so our estimate uses the largest value. The degrees of freedom are n-1. The non-overlapping bars are virtually identical to the ANOVA paired comparisons. Because our example had heterogeneity of variance across groups, we also estimated the means and standard errors directly with a Bayesian Monte Carlo Markov Chain analysis (AMOS 18, IBM Corp) and computed the same confidence interval. In this example both analyses provided very similar results.