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 \subsection{Simulating misclassification in skewed variables}
 
-For simplicity, our main simulations have balanced classified variables.  But classifiers are often used to measure imbalanced or skewed variables, which can be more difficult to predict.  Here, we show that MLE correction performs similarly well as with skewned classified variables. Although the Fischer approximation for confidence intervals performs poorly, the profile likelihood method works well.
+For simplicity, our main simulations have balanced classified variables.  But classifiers are often used to measure imbalanced or skewed variables, which can be more difficult to predict.  Here, we show that MLE correction performs similarly well as with skewed classified variables. Although the Fischer approximation for confidence intervals performs poorly, the profile likelihood method works well.
 
 
 %However, if one can assume the model for $Y$, then one believes that $Y$ and $X$ are conditionally independent given other observed variables.