During the current COVID-19 pandemic, misinformation is a major challenge, raising several social and psychological concerns. This article highlights the prevailing misinformation as an outbreak containing hoaxes, myths, and rumours. In comparison to traditional media, online media platforms facilitate misinformation even more widely. To further affirm this ethical concern, the researchers cite relevant studies demonstrating the role of new media in misinformation and its potential consequences. Besides other significant psychosocial impacts, such as xenophobia, psychological distress, LGBT rights violation, gender-based violence, misinformation is undermining healthcare workers' psychological health and their efforts to mitigate the impact of COVID-19. In view of the adverse consequences of misinformation, this article addresses it as a massive ethical challenge during the current outbreak. Thus, the researchers make relevant suggestions to evaluate misinformation sources and mitigate the psychosocial impacts attributed to misinformation during crises. They include forming mental health teams comprising of psychologists, psychiatrists, and trained paramedical staff; rapid dissemination of authentic and updated COVID-19 situation reports regularly; establishing helpline services; and recognizing a broader range of personal needs. All health authorities should make clear that they are listening and responding to public concerns. Much effort is needed to counteract COVID-19 misinformation.

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This article aims to suggest a new improved generalized class of estimators for finite population distribution function of the study and the auxiliary variables as well as mean of the usual auxiliary variable under simple random sampling. The numerical expressions for the bias and mean squared error (MSE) are derived up to first degree of approximation. From our generalized class of estimators, we obtained two improved estimators. The gain in second proposed estimator is more as compared to first estimator. Three real data sets and a simulation are accompanied to measure the performances of our generalized class of estimators. The MSE of our proposed estimators is minimum and consequently percentage relative efficiency is higher as compared to their existing counterparts. From the numerical outcomes it has been shown that the proposed estimators perform well as compared to all considered estimators in this study.

In this article, we propose an improved estimator for finite population variance based on stratified sampling by using the auxiliary variable as well as the rank of the auxiliary variable. Expressions for the bias and the mean square error of the estimators are derived up to the first order of approximation. Four real data sets are used to measure the performances of estimators. Moreover, a simulation study is also conducted to observe the efficiency of the proposed variance estimator. The theoretical and numerical results show that the proposed estimator under stratified random sampling is more efficient as compared to the existing estimators.

In this study, we propose a new improved estimator of population mean for the sensitive variable in the presence of measurement error under simple and stratified random sampling. This estimator accounts the auxiliary information as well as the ranks of the auxiliary variable. From theoretical and numerical studies it is shown that a new improved estimator performs better than the existing estimators under study.