Jessica Glazier

Despite increasing advocacy for gender equality, gender prejudice and discrimination persist. The origins of these biases develop in early childhood, but it is less clear whether (1) children's gender attitudes predict discrimination and (2) gender attitudes and discrimination vary by ethnicity and US region. We examine these questions with an ethnically (Asian, Black, Latinx and White) and geographically (Northeast, Pacific Northwest, West, Southeast and Hawaii) diverse sample of 4‐ to 6‐year‐old children ( N = 605) who completed measures of gender attitudes and discrimination in a preregistered study. Children, across groups, demonstrated more positive attitudes towards their gender ingroup. Children who showed more pro‐ingroup attitudes also showed more pro‐ingroup behavioural discrimination. Girls showed stronger ingroup favouritism than boys, but ethnic and regional groups generally did not vary in levels of bias. These findings contribute to our understanding of how gender intergroup biases develop and highlight the generalizability of these processes.

Ruling out the possibility that there is absolutely no effect or association between variables may be a good first step, but it is rarely the ultimate goal of science. Yet that is the only inference provided by traditional null hypothesis significance testing (NHST), which has been a mainstay of many scientific fields. Reliance on NHST also makes it difficult to define what it means to replicate a finding, and leads to an uncomfortable quandary in which increasing precision in data reduces researchers' ability to perform theory falsification. To solve these problems, in recent years several alternatives to traditional NHST have been proposed. However, each new test is described using its own terminology and practiced in different fields. We describe a simple, unified framework for conceptualizing all these tests so that it is not necessary to learn them separately. Moreover, the framework allows researchers to conduct any of these tests by asking just one question: is the confidence interval entirely outside the null region(s) ? This framework may also help researchers choose the test(s) that best answers their research question when simply ruling out ‘no effect at all’ is not enough.