Stories with Tag research bias

• How to avoid P hacking

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  Posted: 2025-05-09 07:53:54

  Verbose tl;dr

  To avoid p-hacking, researchers should pre-register their studies, specifying hypotheses, analyses, and data collection methods before looking at the data. This prevents manipulating analyses to find statistically significant (p<0.5) but spurious results. Additionally, focusing on effect sizes rather than just p-values provides a more meaningful interpretation of results, as does embracing open science practices like sharing data and code for increased transparency and reproducibility. Finally, shifting the focus from null hypothesis significance testing to estimation and incorporating Bayesian methods allows for more nuanced understanding of uncertainty and prior knowledge, further mitigating the risks of p-hacking.

  The Nature article "How to avoid P hacking" elaborates on the pervasive problem of p-hacking, also known as data dredging or significance chasing, within scientific research. P-hacking refers to the manipulation, intentional or unintentional, of data analysis procedures to achieve a statistically significant p-value (typically less than 0.05), often considered the gold standard for publication. This manipulation can lead to the publication of spurious findings, undermining the integrity and reliability of scientific literature.

  The article meticulously details various forms this manipulation can take. Researchers might, for instance, explore multiple subgroups within their dataset until they find a statistically significant relationship, neglecting to account for the increased likelihood of false positives due to multiple comparisons. They might also choose to exclude outliers or specific data points without transparently justifying these exclusions, potentially biasing the results. Furthermore, p-hacking can involve stopping data collection once a desired p-value is reached, rather than adhering to a predetermined sample size, thereby artificially inflating the significance of the observed effect. Changing the statistical analysis method mid-stream after observing initial results, also known as "outcome switching," can also be a subtle form of p-hacking.

  The article emphasizes the detrimental consequences of p-hacking. By artificially generating significant results, it leads to the publication of false positive findings, which can then mislead other researchers and impede scientific progress. When these flawed findings are then used as a basis for further research, they can perpetuate a cycle of misinformation and wasted resources. This undermines public trust in science and can even lead to the implementation of ineffective policies based on faulty research.

  The article further provides concrete recommendations for researchers to actively avoid p-hacking and promote robust scientific practices. It strongly advocates for preregistration, where researchers publicly document their hypotheses, methods, and analysis plan before collecting any data. This transparent approach prevents researchers from retroactively fitting their analyses to the data, thereby minimizing the risk of p-hacking. Additionally, the article encourages greater emphasis on effect sizes and confidence intervals, which provide more nuanced information about the strength and reliability of observed effects than p-values alone. Exploring and reporting all analyses performed, even those that did not yield statistically significant results, is also crucial for transparency and prevents the selective reporting of only favorable findings. Finally, the article highlights the importance of replication studies to validate initial findings and ensure the robustness of scientific discoveries. By implementing these practices, researchers can contribute to a more rigorous and trustworthy scientific landscape, minimizing the detrimental impact of p-hacking.

  • p-hacking
  • data dredging
  • statistical significance
  • research bias
  • scientific integrity
  • Research Methods
  • hypothesis testing
  • multiple comparisons problem
  • false positives
  • Reproducibility
  • Statistics
  • Data Analysis
  • Science
  • publication bias

  Summary of Comments ( 78 )
  https://news.ycombinator.com/item?id=43934682

  Verbose tl;dr

  HN users discuss the difficulty of avoiding p-hacking, even with pre-registration. Some highlight the inherent flexibility in data analysis, from choosing variables and transformations to defining outcomes, arguing that conscious or unconscious bias can still influence results. Others suggest focusing on effect sizes and confidence intervals rather than solely on p-values, and emphasizing the importance of replication. Several commenters point out that pre-registration itself isn't foolproof, as researchers can find ways to deviate from their plans or selectively report pre-registered analyses. The cynicism around "publish or perish" pressures in academia is also noted, with some arguing that systemic issues incentivize p-hacking despite best intentions. A few commenters mention Bayesian methods as a potential alternative, while others express skepticism about any single solution fully addressing the problem.

  The Hacker News post titled "How to avoid P hacking" (linking to a Nature article about the same topic) generated a moderate number of comments, mostly focusing on practical advice and limitations of proposed solutions to p-hacking.

  Several commenters emphasized the importance of clearly defined hypotheses before looking at the data, with one pointing out that exploratory data analysis should be kept separate from confirmatory analysis. This commenter argues that exploring data first and then formulating a hypothesis based on interesting findings is inherently problematic. Another commenter suggests that pre-registration of studies, where researchers publicly outline their hypotheses and methods beforehand, is crucial for preventing p-hacking. However, this commenter acknowledges that pre-registration isn't a foolproof solution, as researchers could still manipulate their analyses after seeing the data, even if they've pre-registered.

  Another thread of discussion revolved around the practical challenges of implementing rigorous statistical methods. One commenter highlighted the issue of "researcher degrees of freedom," meaning the numerous decisions researchers make during data analysis (e.g., which variables to include, which outliers to remove) that can subtly bias the results. This commenter suggests that completely eliminating these degrees of freedom is unrealistic, but increased transparency about the analytical choices made can help mitigate the problem.

  The conversation also touched on the limitations of p-values themselves. One commenter mentioned that focusing solely on p-values can lead to misleading conclusions and advocated for using effect sizes and confidence intervals to provide a more comprehensive picture of the results. This commenter also suggested Bayesian methods as a potentially useful alternative to frequentist approaches.

  Another user discussed the pressures faced by researchers to publish statistically significant results, which contribute to the prevalence of p-hacking. This commenter argued that a cultural shift is needed within academia to prioritize rigorous research practices over chasing statistically significant findings.

  Finally, a few comments provided specific examples of p-hacking techniques and discussed how to identify them in published research. One commenter mentioned the practice of "HARKing" (Hypothesizing After the Results are Known), where researchers present post-hoc hypotheses as if they were a priori. Another commenter pointed out that looking at multiple subgroups within a dataset and only reporting the significant findings is a common form of p-hacking.

  In summary, the comments on the Hacker News post offer a practical perspective on the issue of p-hacking, emphasizing the importance of pre-defined hypotheses, transparency in data analysis, the limitations of p-values, and the need for a change in research culture. While the comments largely agree on the problem, they also acknowledge the complexity of implementing perfect solutions.

• How 'animal methods bias' is affecting research careers

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  Posted: 2025-03-21 19:51:42

  Verbose tl;dr

  Researchers reliant on animal models, particularly in neuroscience and physiology, face growing career obstacles. Funding is increasingly directed towards human-focused research like clinical trials and 'omics' approaches, seen as more translatable to human health. This shift, termed "animal methods bias," disadvantages scientists trained in animal research, limiting their funding opportunities, hindering career progression, and potentially slowing crucial basic research. While acknowledging the importance of human-focused studies, the article highlights the ongoing need for animal models in understanding fundamental biological processes and developing new treatments, urging funders and institutions to recognize and address this bias to avoid stifling valuable scientific contributions.

  The Nature article, "How 'animal methods bias' is affecting research careers," delves into the pervasive and often insidious issue of systemic bias against scientists whose research primarily utilizes animal models. This bias, deeply ingrained within the scientific community, manifests in various forms, creating tangible obstacles to career advancement and hindering the overall progress of certain fields of scientific inquiry.

  The piece meticulously outlines how this "animal methods bias" operates at multiple levels, impacting everything from grant funding decisions and publication opportunities to career progression within academic institutions and even the broader recognition of scientific achievements. Specifically, the article details how researchers relying on animal models frequently encounter difficulties securing funding for their projects, facing heightened scrutiny and skepticism compared to researchers employing alternative methodologies. This disparity in funding allocation can severely limit the scope and impact of research involving animal subjects.

  Furthermore, the article explores the challenges faced by researchers when submitting manuscripts to scientific journals. It highlights the inherent biases within the peer-review process, where reviewers, often subconsciously, favor studies using non-animal methods, potentially leading to the rejection of high-quality research solely based on methodological grounds. This publication bias further marginalizes researchers working with animal models and impedes the dissemination of valuable scientific findings.

  Beyond funding and publication, the article examines the broader impact of this bias on career trajectories. Researchers reliant on animal models may find themselves disadvantaged when competing for promotions, academic appointments, and prestigious awards. This systemic disadvantage creates a chilling effect, potentially dissuading young scientists from pursuing research involving animal models altogether, thus narrowing the field of potential breakthroughs in areas critically dependent on such research, including neuroscience, physiology, and various branches of medicine.

  The article also underscores the ethical complexities surrounding animal research, acknowledging the importance of minimizing animal suffering and adhering to strict ethical guidelines. However, it stresses the crucial role animal models continue to play in advancing scientific knowledge and developing life-saving treatments, emphasizing that dismissing this methodology entirely would severely hamper progress in numerous fields. Ultimately, the article calls for a more nuanced and balanced approach, urging the scientific community to recognize and address the pervasive bias against animal research, fostering a more inclusive and equitable environment that values diverse research approaches and promotes scientific progress across all methodologies. It argues that overcoming this bias is essential for unlocking the full potential of scientific discovery and maximizing the benefits of research for the betterment of human and animal health.

  • animal research
  • research bias
  • Career Impact
  • scientific funding
  • Research Methodology
  • animal models
  • alternative methods
  • scientific careers
  • research ethics
  • preclinical research
  • publication bias
  • Reproducibility
  • 3Rs principles
  • Animal Welfare

  Summary of Comments ( 3 )
  https://news.ycombinator.com/item?id=43440143

  Verbose tl;dr

  HN commenters discuss the systemic biases against research using animal models. Several express concern that the increasing difficulty and expense of such research, coupled with the perceived lower status compared to other biological research, is driving talent away from crucial areas of study like neuroscience. Some note the irony that these biases are occurring despite significant breakthroughs having come from animal research, and the continued need for it in many fields. Others mention the influence of animal rights activism and public perception on funding decisions. One commenter suggests the bias extends beyond careers, impacting publications and grant applications, ultimately hindering scientific progress. A few discuss the ethical implications and the need for alternatives, acknowledging the complex balancing act between animal welfare and scientific advancement.

  The Hacker News post "How 'animal methods bias' is affecting research careers" (https://news.ycombinator.com/item?id=43440143) has generated a moderate number of comments discussing the article from Nature. The discussion centers around the challenges faced by researchers who don't primarily use animal models, particularly in securing funding and career advancement.

  Several commenters share personal anecdotes corroborating the article's claims. One commenter describes their struggles in obtaining grants for non-animal research, even when proposing alternative methods like organ-on-a-chip technology. They highlight the inherent bias in the review process where reviewers often default to animal models, potentially due to familiarity and established protocols. This bias, they argue, creates a significant hurdle for researchers exploring innovative and potentially more ethical research avenues.

  Another commenter points out the "lock-in" effect of animal research, where existing infrastructure and established expertise make it easier to continue funding projects reliant on these models. This creates a cycle where non-animal methods struggle to gain traction due to a lack of funding and, consequently, a dearth of trained researchers.

  The discussion also touches upon the potential limitations of relying solely on animal models. One commenter notes the issue of translatability—the difficulty of reliably extrapolating findings from animal studies to humans. They suggest that diversifying research approaches, including in vitro and in silico methods, could lead to more relevant and accurate results.

  Furthermore, the financial implications of animal research are raised. One commenter mentions the high cost of maintaining animal facilities and conducting animal studies, posing the question of whether these resources could be more effectively allocated to alternative methods.

  The ethical considerations surrounding animal research also feature in the discussion, albeit less prominently. While some acknowledge the ethical dilemmas inherent in using animals for research, the primary focus of the comments remains on the career implications of the "animal methods bias".

  Finally, there's some discussion about potential solutions. One suggestion involves increasing transparency in grant review processes to identify and mitigate bias. Another proposes actively promoting and funding the development and validation of alternative research methods.

  In summary, the comments on Hacker News largely echo and expand upon the themes presented in the Nature article. Commenters offer personal experiences, discuss systemic issues contributing to the bias, highlight the limitations of animal models, and propose potential solutions to level the playing field for researchers exploring alternative methods. While ethical concerns are touched upon, the discussion predominantly revolves around the practical and career-related consequences of the prevailing bias towards animal-based research.