A focus on novel, confirmatory, and statistically significant results leads to substantial bias in the scientific literature. One type of bias, known as “p-hacking,” occurs when researchers collect or select data or statistical analyses until nonsignificant results become significant. Here, we use text-mining to demonstrate that p-hacking is widespread throughout science. We then illustrate how one can test for p-hacking when performing a meta-analysis and show that, while p-hacking is probably common, its effect seems to be weak relative to the real effect sizes being measured. This result suggests that p-hacking probably does not drastically alter scientific consensuses drawn from meta-analyses.

Sexual selection is a major force behind the rapid evolution of male genital morphology among species. Most within-species studies have focused on sexual selection on male genital traits owing to events during or after copulation that increase a male's share of paternity. Very little attention has been given to whether genitalia are visual signals that cause males to vary in their attractiveness to females and are therefore under pre-copulatory sexual selection. Here we show that, on average, female eastern mosquitofish Gambusia holbrooki spent more time in association with males who received only a slight reduction in the length of the intromittent organ (‘gonopodium’) than males that received a greater reduction. This preference was, however, only expressed when females chose between two large males; for small males, there was no effect of genital size on female association time.

Sexual selection is a cornerstone of evolutionary theory, but measuring it has proved surprisingly difficult and controversial. Various proxy measures-e.g., the Bateman gradient and the opportunity for sexual selection-are widely used in empirical studies. However, we do not know how reliably these measures predict the strength of sexual selection across natural systems, and most perform poorly in theoretical worst-case scenarios. Here we provide a rigorous comparison of eight commonly used indexes of sexual selection. We simulated 500 biologically plausible mating systems, based on the templates of five well-studied species that cover a diverse range of reproductive life histories. We compared putative indexes to the actual strength of premating sexual selection, measured as the strength of selection on a simulated "mating trait." This method sidesteps a key weakness of empirical studies, which lack an appropriate yardstick against which proxy measures can be assessed. Our model predicts that, far from being useless, the best proxy measures reliably track the strength of sexual selection across biologically realistic scenarios. The maximum intensity of precopulatory sexual selection s′ max (the Jones index) outperformed all other indexes and was highly correlated with the strength of sexual selection. In contrast, the Bateman gradient and the opportunity for sexual selection were poor predictors of sexual selection, despite their continuing popularity.

Sex allocation theory explains why most species produce equal numbers of sons and daughters, and highlights situations that select for deviation from this norm. Past research has, however, heavily focused on situations with discrete generations. When temporally varying generational overlap affects future mate availability, models predict cyclical shifts in sex allocation, but these predictions have not yet been appropriately tested. Here we provide evidence that mosquitofish (Gambusia holbrooki) populations possess a suitable life history: some autumn-born females bred alongside their own offspring, while such overlap was rare or absent for spring-born females and for all males. Our analytic model of sex allocation for these populations produced a perfect rank-order correlation between observed birth sex ratio biases and theoretical predictions, with stronger biases observed as the extent of female generational overlap increased. This is the first robust evidence that sex allocation theory accounts for cases when mating opportunities vary predictably over time.

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