Amazon Mechanical Turk (MTurk) is a crowdsourcing platform that has become a popular tool for behavioral research. However, researchers have overlooked key differences between MTurk and traditional recruitment methods, which can affect data quality and validity. This article discusses the challenges of nonnaïveté among MTurk workers and how researchers can address these issues. MTurk workers are more likely to participate in multiple related experiments, and researchers may be too eager to exclude participants. This can lead to biased data and undermine the assumptions of experimental research methods, such as random assignment and independence of observations. To address these issues, researchers can use MTurk's Qualification system to prescreen workers and manage their inclusion and exclusion over the course of a study. Workers may also share information with each other, which can lead to foreknowledge of experiments and affect data validity. Researchers should be aware of this and take steps to minimize the impact of worker cross-talk. Additionally, researchers should be cautious about data cleaning practices, as they may be more zealous than necessary, leading to biased data. The article also discusses the prevalence of duplicate responses and the importance of identifying and excluding them. It highlights the need for researchers to carefully consider the population of interest before data collection and to use prescreening techniques to ensure the quality of data. Overall, MTurk has the potential to be a valuable tool for behavioral research, but researchers must be aware of the challenges and take steps to ensure the validity of their data. By using prescreening techniques and being cautious about data cleaning practices, researchers can maximize the benefits of MTurk while minimizing the risks.Amazon Mechanical Turk (MTurk) is a crowdsourcing platform that has become a popular tool for behavioral research. However, researchers have overlooked key differences between MTurk and traditional recruitment methods, which can affect data quality and validity. This article discusses the challenges of nonnaïveté among MTurk workers and how researchers can address these issues. MTurk workers are more likely to participate in multiple related experiments, and researchers may be too eager to exclude participants. This can lead to biased data and undermine the assumptions of experimental research methods, such as random assignment and independence of observations. To address these issues, researchers can use MTurk's Qualification system to prescreen workers and manage their inclusion and exclusion over the course of a study. Workers may also share information with each other, which can lead to foreknowledge of experiments and affect data validity. Researchers should be aware of this and take steps to minimize the impact of worker cross-talk. Additionally, researchers should be cautious about data cleaning practices, as they may be more zealous than necessary, leading to biased data. The article also discusses the prevalence of duplicate responses and the importance of identifying and excluding them. It highlights the need for researchers to carefully consider the population of interest before data collection and to use prescreening techniques to ensure the quality of data. Overall, MTurk has the potential to be a valuable tool for behavioral research, but researchers must be aware of the challenges and take steps to ensure the validity of their data. By using prescreening techniques and being cautious about data cleaning practices, researchers can maximize the benefits of MTurk while minimizing the risks.