Outcome misclassification occurs when a categorical variable is incorrectly assigned to a group due to an imperfect diagnostic device. The failure to account for outcome misclassification results in biased estimation and inaccurate predictions. This dissertation focuses on correcting outcome misclassification in causal inference and spatial classification. In the first work, we describe a Bayesian propensity score analysis to estimate the causal effect in observational studies with misclassified multinomial outcomes. To adjust the effect of misclassification, the informative Dirichlet priors are specified based on previous studies. Taking misclassification into account significantly reduces bias and yields coverage probabilities closer to a given nominal value. In spatial classification, we propose validation data-based adjustment methods using interval validation data. Regression calibration (RC) and multiple imputation (MI) are utilized to correct misclassified outcomes where a gold-standard device is not available. Spatial generalized linear mixed model (SGLMM) and indicator kriging (IK) are applied to spatial classification at unsampled locations. In a Bayesian perspective, we propose two-stage methods incorporating validation data into prior elicitation. The proposed Frequentist and Bayesian methods significantly improve the spatial classification accuracy.