It is a crime to plot measures of central tendency without an indication of their variability. Enough said! II. What do we use as errorbars? There are pretty much two options: standard errors, or confidence intervals. These quantities are related. The confidence interval is the standard error multiplied by the critical value of a test statistic, which is either t or Z, depending on whether we know the population parameters or estimate them from a sample. The choice really depends upon your rhetorical intent: different things can be concluded from the errorbars, depending on what you choose to plot. Standard
errors III. Errorbars for betweensubject means We have two ways of estimating the standard error: a local and a global estimate. Again, it's up to you which one you use. If you're going to be using withinsubjects errorbars subsequently, then it's best to use the global estimate for consistency. Local estimate
of the standard error IV. Errorbars for withinsubject means The trick is to think about what is the best estimate of the error variance. When you do a withinsubjects ANOVA, the analogue of the MSE is the mean square for the interaction of subjects and the effect you're testing. Basically, if you want to show differences between means on the basis of some factor, replace the MSE in the equation for betweensubject means with whatever appears in the denominator of your withinsubjects Fratio. Binomial
data In general, we have Multinomial
data Written by Tom Griffiths 
