Determining confidence intervals on psychophysical thresholds is straight forward if the psychometric function is known. In clinical settings, however, there is only partial information about the psychometric function, hence confidence limits are usually derived from test-retest data collected from many subjects. In this paper, we introduce a computational technique for deriving confidence limits for an individual's endpoint threshold using data typically obtained in a clinical setting, rather than a database of test-retest performance. The technique uses probabilistic analysis of all possible response sequences in a test procedure. We then extend this procedure to allow for levels of typical uncertainty in data measurement.