In this review, we describe the sex differences in prevalence, onset, symptom profiles, and disease outcome that are evident in schizophrenia, bipolar disorder, and post‐traumatic stress disorder. Women with schizophrenia tend to exhibit less disease impairment than men. By contrast, women with post‐traumatic stress disorder are more affected than men. The most likely candidates to explain these sex differences are gonadal hormones. This review details the clinical evidence that oestradiol and progesterone are dysregulated in these psychiatric disorders. Notably, existing data on oestradiol, and to a lesser extent, progesterone, suggest that low levels of these hormones may increase the risk of disease development and worsen symptom severity. We argue that future studies require a more inclusive, considered analysis of gonadal steroid hormones and the intricacies of the interactions between them, with methodological rigour applied, to enhance our understanding of the roles of steroid hormones in psychiatric disorders.LINKED ARTICLES: This article is part of a themed section on The Importance of Sex Differences in Pharmacology Research. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v176.21/issuetoc

Fear conditioning and extinction is a construct integral to understanding trauma-, stress- and anxiety-related disorders. In the laboratory, associative learning paradigms that pair aversive with neutral stimuli are used as analogues to real-life fear learning. These studies use physiological indices, such as skin conductance, to sensitively measure rates and intensity of learning and extinction. In this review, we discuss some of the potential limitations in interpreting and analysing physiological data during the acquisition or extinction of conditioned fear. We argue that the utmost attention should be paid to the development of modelling approaches of physiological data in associative learning paradigms, by illustrating the lack of replicability and interpretability of results in current methods. We also show that statistical significance may be easily achieved in this paradigm without more stringent data and data analysis reporting requirements, leaving this particular field vulnerable to misleading conclusions. This review is written so that issues and potential solutions are accessible to researchers without mathematical training. We conclude the review with some suggestions that all laboratories should be able to implement, including visualising the full data set in publications and adopting modelling, or at least regression-based, approaches.