We consider ratio estimators in simple random sampling under data anomalies. We specifically focus on the situations where the error term is not normally distributed, and exploit Tiku’s modified maximum likelihood estimators in the ratio method of estimation. We derive the mean square errors of the proposed ratio estimators theoretically and obtain the conditions where the proposed ratio estimators have less mean square errors than the traditional ratio-type estimators. We support our theoretical results with two different real-life examples.