Scientific interest in whether women experience changes across the ovulatory cycle in mating-related motivations, preferences, cognitions, and behaviors has surged in the past 2 decades. A prominent hypothesis in this area, the ovulatory shift hypothesis, posits that women experience elevated immediate sexual attraction on high- relative to low-fertility days of the cycle to men with characteristics that reflected genetic quality ancestrally. Dozens of published studies have aimed to test this hypothesis, with some reporting null effects. We conducted a meta-analysis to quantitatively evaluate support for the pattern of cycle shifts predicted by the ovulatory shift hypothesis in a total sample of 134 effects from 38 published and 12 unpublished studies. Consistent with the hypothesis, analyses revealed robust cycle shifts that were specific to women's preferences for hypothesized cues of (ancestral) genetic quality (96 effects in 50 studies). Cycle shifts were present when women evaluated men's "short-term" attractiveness and absent when women evaluated men's "long-term" attractiveness. More focused analyses identified specific characteristics for which cycle shifts were or were not robust and revealed areas in need of more research. Finally, we used several methods to assess potential bias due to an underrepresentation of small effects in the meta-analysis sample or to "researcher degrees of freedom" in definitions of high- and low-fertility cycle phases. Neither type of bias appeared to account for the observed cycle shifts. The existence of robust relationship context-dependent cycle shifts in women's mate preferences has implications for understanding the role of evolved psychological mechanisms and the ovulatory cycle in women's attractions and social behavior.

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In contrast to our closest cousin, the chimpanzee, humans appear at first to lack cues of impending ovulation that would mark the fertile period in which a female can become pregnant. Consequently, that ovulation is “concealed” in women has long been the consensus among scientists studying human mating. A recent series of studies shows, however, that there are discernible cues of fertility in women’s social behaviors, body scents, voices, and, possibly, aspects of physical beauty. Some of these changes are subtle, but others are strikingly large (we report effect sizes ranging from small, d = 0.12 to large, d = 1.20). Moreover, emerging evidence suggests that women’s male partners may adaptively shift their behavior in response to cues of approaching ovulation. These results have far-reaching implications for understanding fluctuations in attraction, conflict, and relationship dynamics.

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Two meta-analyses evaluated shifts across the ovulatory cycle in women's mate preferences but reported very different findings. In this journal, we reported robust evidence for the pattern of cycle shifts predicted by the ovulatory shift hypothesis (Gildersleeve, Haselton, & Fales, 2014). However, Wood, Kressel, Joshi, and Louie (2014) claimed an absence of compelling support for this hypothesis and asserted that the few significant cycle shifts they observed were false positives resulting from publication bias, p-hacking, or other research artifacts. How could 2 meta-analyses of the same literature reach such different conclusions? We reanalyzed the data compiled by Wood et al. These analyses revealed problems in Wood et al.'s meta-analysis-some of which are reproduced in Wood and Carden's (2014) comment in the current issue of this journal-that led them to overlook clear evidence for the ovulatory shift hypothesis in their own set of effects. In addition, we present right-skewed p-curves that directly contradict speculations by Wood et al.; Wood and Carden; and Harris, Pashler, and Mickes (2014) that supportive findings in the cycle shift literature are false positives. Therefore, evidence from both of the meta-analyses and the p-curves strongly supports genuine, robust effects consistent with the ovulatory shift hypothesis and contradicts claims that these effects merely reflect publication bias, p-hacking, or other research artifacts. Unfounded speculations about p-hacking distort the research record and risk unfairly damaging researchers' reputations; they should therefore be made only on the basis of firm evidence.