The population parameter K, which represents the ideal value for a class of ratio-type estimators in finite populations, is the main subject of this paper. Under simple random sampling, where each population unit has an equal chance of being selected, we provide an almost unbiased estimate for K. With modifications intended to lessen bias while maintaining efficiency, the suggested estimator is based on the class first presented by Srivastava et al. (1986). In sampling theory, unbiased estimators are essential because they guarantee that the estimate's predicted value matches the actual population parameter. Nevertheless, completely unbiased estimators may have excessive variance or be unworkable. By lowering systematic error while preserving a respectable level of precision, nearly unbiased estimators provide a useful substitute in certain situations. These estimators are very helpful for generating consistent and dependable findings while sampling a limited population. Two actual datasets and one simulated dataset were used to assess the suggested nearly unbiased estimate for K. Percent relative efficiency (PRE) and mean squared error (MSE) were used to evaluate performance. According to the results, up to the first-order approximation, the suggested estimator is more effective and almost unbiased.
