The current research examines a contentious issue: Can the political composition of a scholarly field undercut the scientific rigor of the research? To address this question, we analyzed a set of nearly 200 psychology studies and subsequent replication attempts. Although there are many more psychologists who are liberal, the results in the literature did not completely mirror the heavy political skew of psychologists. Whereas there were more findings consistent with a liberal worldview than a conservative worldview, the average ideology of research was fairly centrist, and the majority of research was either nonpolitical (48% according to doctoralcoder ratings) or politically relevant but without a clear political slant (74% among the politically relevant subset, according to doctoral-coder ratings). More importantly, liberal findings were just as likely to be replicable and, in exploratory analyses, were as statistically robust as conservative findings and as likely to be cited or mentioned in the media. These results remained consistent across both liberal, moderate, and conservative coders; expert and lay coders; and when numerous covariates known to account for replicability were added to our statistical models. Instead, we found mixed evidence of a political extremity effect, such that research that was more politically slanted (regardless of liberal vs. conservative political slant) was between 34% (Study 1) and 6% (Study 2) less likely to be replicated. These results were stable across all model specifications that statistically adjusted for variables associated with statistical robustness in Study 1, but in Study 2, the effect of political extremity was reduced when statistically adjusting for variables related to statistical robustness. On one hand, the preliminary political extremity effect in our data accords with concerns about highly politicized research (Tetlock, 1994). On the other hand, the majority of our analyses suggest that statistical robustness is the consistent predictor of replicability rather than the political slant or extremity of a research topic. These results suggest that it is important to focus on study characteristics such as sample size and effect size to help improve replicability. Moreover, we urge caution in interpreting our political extremity effect given that most studies in our database were not ideologically extreme and that there may be a restriction of range. Although we did not find evidence of a liberal bias in scientific replicability in these data, perceptions of political bias still exist both among laypeople (Hannikainen, 2019) and academics. For example, Eitan and colleagues (2018) found that academics (students and professors) believed that personal political beliefs slightly bias scientific research (Pearson’s r = .62) and that social psychology is biased against conservatives (Pearson’s r = .83). The fact that we find some evidence of a political extremity effect coupled with the fact that there tends to be more liberal-leaning research in psychology offers one possible explanation. If highly political research is less replicable but people are sampling only one side of the political spectrum because of a shifted distribution of published research, it would appear rational to arrive at the conclusion that liberalleaning research is less robust. Of course, our findings suggest that such an asymmetric sampling may inadvertently miss the possibility that the root cause is in fact a symmetric political bias in scientific replicability. Moreover, when scientists use the word bias, they often mean different things at different times. For example, bias may refer to the skewed political distribution of psychologists themselves or the possible tendency to study certain topics (although our distributions of political slant were fairly normal). Whereas Duarte and colleagues (2015) hypothesized reasons for the large number of liberals in the field (see also Haidt, 2011), our data suggest that the political skew of psychologists is not tightly coupled with the political skew of the literature itself, and future work should seek to disentangle these discrepancies. In other instances, bias might refer to the systematic tendency to evaluate research differently on the basis of its political slant. Duarte and colleagues (2015) cited evidence of such a peer-review political bias from Abramowitz, Gomes, and Abramowitz (1975). Yet that study had methodological shortcomings,22 and even the authors themselves admitted that “the amount of bias detected might be so slight as to be meaningless in the real world of publish or perish” (p. 193). In fact, similar tentative evidence of peer-review bias has even been found against liberal-leaning diversity research (King, Avery, Hebl, & Cortina, 2018) and gender-bias research (Cislak, Formanowicz, & Saguy, 2018). This suggests that both liberal and conservative perspectives may experience subtle bias but that, overall, the peer-review process mitigates most egregious instances of political favoritism.23 Still, some data suggest that discrimination based on the political orientation of research may exist. For instance, a survey of 292 members of the Society for Personality and Social Psychology found that respondents self-reported a willingness to discriminate against a hypothetical grant application or manuscript, at least to some minimal extent (i.e., chose a scale point above “not at all”), if there was a feeling that it took a “politically conservative perspective” (Inbar & Lammers, 2012). However, our data suggest that tenuous liberal research does not systematically find its way into the published literature. Duarte and colleagues (2015) acknowledged that “the lack of political diversity is not a threat to the validity of specific studies in many and perhaps most areas of research in social psychology” (p. 2). It remains possible that their claims may apply to a very small subsection of psychology, if any, given that we found no evidence that research aligned with a majority viewpoint (liberalism) was less replicable or less statistically robust than research aligned with a minority viewpoint (conservatism). Although our data, to our knowledge, provide the first test of whether the political slant of research is associated with scientific replicability, there are a number of limitations to our work. One important limitation is that our sample was not a random sample of the entire field of psychological research. Although the largest database we used was intentionally designed to sample a relatively representative group of high-impact psychology articles (OSC, 2015), our sample was nevertheless limited to studies for which replication data were readily available and thus was not completely representative of the entire field. Although selection biases could occur, there are at least two possible counterarguments mitigating this issue. First, given that psychologists have historically prioritized surprising results, it might be more likely that replicators would choose studies that surprised them or about which they were skeptical (e.g., studies that did not align with their own personal political ideology). With a predominantly liberal field, conservative findings would be most surprising. Second, many of the studies selected for replication were chosen because they represent some of the most influential findings in psychology (e.g., APS RRRs) or were specifically chosen to reflect a range of effects and contexts (e.g., Many Labs). Therefore, replicators made explicit efforts to identify a combination of important and representative research. Future research should examine whether these findings extend to other areas of psychology as well as other social sciences because larger and more representative samples will be more likely to produce generalizable knowledge. In addition, our analyses examining the association between political slant and statistical robustness or postpublication impact could also be performed on a much broader swath of the literature if future scholars are willing to code political slant for more studies. Such an analysis would be useful for future research. A second important limitation is that measuring political slant is challenging. Labels such as “liberal” or “conservative” may be too broad to capture the nuanced ideologies and assorted political attitudes of people (Ditto et al., 2018). For example, we did not differentiate among the political slants for social, economic, or foreign-policy subcategories (Inbar & Lammers, 2012). In addition, we decided on using the binary political spectrum of American politics, which is quite common (Inglehart & Klingemann, 1976; Jost, Glaser, Kruglanski, & Sulloway, 2003) but is still debated among political scientists (Feldman & Johnston, 2014). Moreover, political contexts and relative ideologies rapidly shift, and what we refer to as liberal today may differ from its usage 50 years ago (e.g., many older liberals might claim that current mainstream liberals are quite moderate relative to the 1960s). Thus, our data speak to the current construction of American politics. In addition, the specific political slant of many studies was not clear-cut in many cases, as reflected by our lower political-slant reliability across coders. This suggests that debates about political bias may hinge on idiosyncratic definitions rather than a clear, shared definition of ideology that can be easily observed and coded. Future research should further clarify political slant and continue to pursue additional operationalizations of political slant to accumulate evidence. It is unclear whether the personal political beliefs of scientists have a measurable influence on the replicability and robustness of the published literature. The peerreview process may be sufficient to weed out most political manuscripts that are not backed by sufficient scientific data, and scientists may be more motivated by scientific identities and norms when they are writing and reviewing manuscripts (Merton, 1973; Van Bavel et al., 2020). In fact, the identity of scientist is more likely to be salient during this process, which can help reduce motivated political cognition (Van Bavel & Pereira, 2018). These features can help mitigate political groupthink (Van Bavel et al., 2020). However, bias of various forms can still emerge, and it is unclear from our data when people can or do pursue value-free science (Longino, 1990; Richardson & Polyakova, 2012; Rykiel, 2001; Sears, 1994). Indeed, we are all shaped by our experience, and science cannot avoid at least some aspect of subjectivity. For example, it is possible that any ideological biases that affected the original research (e.g., measurement strategies) were simply carried over in a direct replication, which yields subjective bias on both ends of the scientific process. Thus, we believe that pursuing adversarial collaborations (Shi, Teplitskiy, Duede, & Evans, 2019) and performing “turnabout” tests, wherein a hypothesis is inverted to test a reverse claim, may be a helpful guard against confirmation bias and groupthink (Duarte et al., 2015; McGuire, 1997; Washburn & Skitka, 2018). The current research also speaks to the quality and replicability of research more broadly. Sparked by difficulty in replicating findings in genetics (Hirschhorn, Lohmueller, Byrne, & Hirschhorn, 2002), pharmacology (Prinz, Schlange, & Asadullah, 2011), oncology (Begley & Ellis, 2012), biology (Reaves, Sinha, Rabinowitz, Kruglyak, & Redfield, 2012), psychology (OSC, 2015), and economics (Chang & Li, 2015), researchers have turned the microscope on themselves and started a dialogue about best research practices. Some of science’s most well-known journals (e.g., Nature and Science) and funding agencies have called for more replications and implemented new procedures to enhance the robustness of published research (Baker, 2016; Bollen, Cacioppo, Kaplan, Krosnick, & Olds, 2015; McNutt, 2014; Nature, 2013, 2017). Many factors reduce replicability, including the publication of false positives (Cohen, 1992; Simmons, Nelson, & Simonsohn, 2011), publication bias (Ferguson & Heene, 2012), and low-fidelity replications (Gilbert, King, Pettigrew, & Wilson, 2016). The current research suggests that research that is more politically extreme may be another factor associated with reduced replication rates. However, this preliminary result applies equally to liberal and conservative findings and may be partially related to the contextual sensitivity of highly political findings (Crawford, Vodapalli, Stingel, & Ruscio, 2019; Van Bavel et al., 2016). Indeed, several politically relevant effects have fluctuated over time (e.g., flag priming, the “Obama effect”), and meta-analyses that have examined politically loaded topics have revealed a large degree of heterogeneity in effect sizes, possibly because findings change as a function of the broader political context (e.g., legislation, governmental actions; e.g., Tankard & Paluck, 2017). Future work would do well to include larger samples of replication studies as they become available to see if our findings are robust under conditions of increased statistical power. Taken together, our results are a starting point for a richer conversation about the role and influence of politics in science. It seems that our intuitions about political bias may at times be imprecise (Eitan et al., 2018). Our findings provide clear evidence that statistical robustness (e.g., sample size and effect size) is a consistent predictor of replicability rather than the political slant or extremity of a research topic. Thus, it might be more fruitful to shift our focus from the politics of scientists to their research practices. We hope other researchers can build off of our work because these issues are critical for scientists’ epistemological pursuits.