Update 'error' handling sections

protocol.tex

@@ -183,6 +183,12 @@ publication bias. Thus, larger studies with $n_i = 500$ are always accepted.
 As described in Section~\ref{sec:scenario}, we set $\theta \in \{0.1, 0.2, 0.5\}$.
 See the R function \texttt{simREbias()}.
 
+In order to check how this implementation of publication bias impacts the
+simulation performance, we keep track of the mean acceptance probability for
+each simulation scenario that is subject publication bias. For the calculation
+of the mean, we also consider large studies with $n = 500$. Since such studies
+are not subject to publication bias, they have an acceptance probability of 1.
+
 \subsection{Simulation procedure}
 For each scenario in Section~\ref{sec:scenario} we
 \begin{enumerate}
@@ -250,7 +256,7 @@ We assess the CIs using the following criteria
     0. % n
 \end{enumerate}
 
-Furthermore, we calculate the following measure related to the point estimates.
+Furthermore, we calculate the following measures related to the point estimates.
 
 \begin{enumerate}
   \item Mean squared error (MSE) of the estimator.
@@ -313,9 +319,13 @@ in the respective paragraph of Subsection~\ref{sec:meas}. We calculate the
 relative frequencies of the number of intervals $m=0, 1, \ldots, 9, >9$ in each
 confidence set over the 10'000 iterations of the same scenario.
 
+Furthermore, we store the mean of the average acceptance probability in each
+of the 10'000 iterations for all simulation scenarios where there is either
+'modest' or 'strong' publication bias.
+
 \section{Presentation of the simulation results}
 For each of the performance measures 1-3 in Subsection~\ref{sec:meas} as well as
-the mean squared error (MSE) we construct plots with
+the mean squared error (MSE), bias, and variance we construct plots with
 
 \begin{itemize}
 \item the number of studies $k$ on the $x$-axis
@@ -324,8 +334,8 @@ the mean squared error (MSE) we construct plots with
 \item one panel for each CI method
 \end{itemize}
 
-Regarding the distribution of the $p$-value function for the harmonic mean
-and $k$-trials methods, we will create plots that contain
+Regarding the distribution of the $p$-value function for the \emph{Edgington}
+and \emph{Fisher} methods, we will create plots that contain
 \begin{itemize}
 \item the number of studies $k$ on the $x$-axis
 \item the value of the summary statistic on the $y$-axis