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We have suggested an improved ratio type log estimator for population variance by using coefficient of kurtosis and median of an auxiliary variable x. The properties of proposed estimator have been derived up to first order of Taylor’s series expansion. The efficiency conditions derived theoretically under which the proposed estimator performs better than existing estimators. The proposed estimator as illustrated by the empirical studies using real populations performs better than the existing estimators i.e. it has the smallest Mean Squared Error and the highest Percentage Relative Efficiency.

We have suggested an improved ratio type log estimator for population variance by using coefficient of kurtosis and median of an auxiliary variable x. The properties of proposed estimator have been derived up to first order of Taylor?s series expansion. The efficiency conditions derived theoretically under which the proposed estimator performs better than existing estimators. The proposed estimator as illustrated by the empirical studies using real populations performs better than the existing estimators i.e. it has the smallest mean squared error and the highest Percentage Relative Efficiency.