observable quantities, that can be used to infer an expert’s beliefs about an uncertain variance.

To the best of our knowledge, there has been little work on eliciting beliefs about variances. One existing approach that can be used is based on eliciting beliefs about parameters in linear regression models. [@KADANE:1980] and [@Al-Awadhi:1998] consider elicitation for the parameters in models of the form $$X_i = \mu + \sum_{j=1}^{p} \beta_{j} z_{ij} + \varepsilon_i, \hspace{0.2 in} \mbox{for} \hspace{ 0.1 in} i = 1,2,...,n,$$ where $\varepsilon_1, ..., \varepsilon_n \overset{\emph{iid}}{\thicksim}  \mbox{N} (0, \sigma^2)$. By setting $\beta_j=0$ for all $j$, this reduces to our case. [@Al-Awadhi:1998] proposed an elicitation method for quantifying opinions about the parameters of a multivariate normal distribution; the same elicitation method could be used for quantifying beliefs about a univariate normally distributed population. A key feature of these methods is asking the expert to update his or her judgements in light of given hypothetical data, under the assumption that the expert updates his or her beliefs using Bayes’ theorem. We think this is a difficult task: the expert may not view hypothetical data as credible and behave the same way had he/she observed real data, and it is unlikely that the expert 