The study examines the effectiveness of two methods for ensuring data quality on Amazon Mechanical Turk (MTurk): attention check questions (ACQs) and restricting participation to workers with high reputation (above 95% approval ratings). Two experiments were conducted to compare these methods in terms of data quality, reliability, and replicability. The results show that high-reputation workers rarely failed ACQs and provided higher-quality data compared to low-reputation workers. ACQs improved data quality only for low-reputation workers in some cases. The second experiment replicated these findings and suggested that more productive high-reputation workers produce the highest quality data. The study concludes that sampling high-reputation workers can ensure high-quality data without the need for ACQs, which may lead to selection bias if participants who fail ACQs are excluded post-hoc.The study examines the effectiveness of two methods for ensuring data quality on Amazon Mechanical Turk (MTurk): attention check questions (ACQs) and restricting participation to workers with high reputation (above 95% approval ratings). Two experiments were conducted to compare these methods in terms of data quality, reliability, and replicability. The results show that high-reputation workers rarely failed ACQs and provided higher-quality data compared to low-reputation workers. ACQs improved data quality only for low-reputation workers in some cases. The second experiment replicated these findings and suggested that more productive high-reputation workers produce the highest quality data. The study concludes that sampling high-reputation workers can ensure high-quality data without the need for ACQs, which may lead to selection bias if participants who fail ACQs are excluded post-hoc.