--- title: Bias Mitigation description: This methodology focuses on identifying and reducing systematic errors and prejudices in scientific research and data analysis. Learners will understand how to design objective experiments, implement blinding techniques, and apply statistical corrections to ensure valid results. category: sciences subcategory: scientific-method difficulty: beginner, intermediate, advanced url: /subject/bias-mitigation --- # Bias Mitigation This methodology focuses on identifying and reducing systematic errors and prejudices in scientific research and data analysis. Learners will understand how to design objective experiments, implement blinding techniques, and apply statistical corrections to ensure valid results. ## Available Resources 1 Videos • 1 Books • 3 Courses • 6 Websites • 3 Papers ## Courses ### 1. Improving your statistical inferences This course aims to help you to draw better statistical inferences from empirical research. First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. Then, you will learn how to design experiments where the false positive rate is controlled, and how to decide upon the sample size for your study, for example in order to achieve high statistical power. Subsequently, you will learn how to interpret evidence in the scientific literature given widespread publication bias, for example by learning about p-curve analysis. Finally, we will talk about how to do philosophy of science, theory construction, and cumulative science, including how to perform replication studies, why and how to pre-register your experiment, and how to share your results following Open Science principles. In practical, hands on assignments, you will learn how to simulate t-tests to learn which p-values you can expect, calculate likelihood ratio's and get an introduction the binomial Bayesian statistics, and learn about the positive predictive value which expresses the probability published research findings are true. We will experience the problems with optional stopping and learn how to prevent these problems by using sequential analyses. You will calculate effect sizes, see how confidence intervals work through simulations, and practice doing a-priori power analyses. Finally, you will learn how to examine whether the null hypothesis is true using equivalence testing and Bayesian statistics, and how to pre-register a study, and share your data on the Open Science Framework. All videos now have Chinese subtitles. More than 30.000 learners have enrolled so far! If you enjoyed this course, I can recommend following it up with me new course "Improving Your Statistical Questions" **Difficulty:** Intermediate | **Price:** Free **Link:** https://www.coursera.org/learn/statistical-inferences **Tags:** statistical-inference, p-values, statistical-power, open-science, research-methods ### 2. Calling Bullshit: Data Reasoning in a Digital World **Author:** Carl T. Bergstrom, Jevin D. West University of Washington course with free video lectures, syllabus, and case studies on detecting misleading quantitative claims. Covers publication bias, selection effects, correlation versus causation, and deceptive data visualisation. Lectures 4 and 7 (statistical traps, publication bias) are taught with real published examples, and the material is a genuine credit-bearing UW course rather than repackaged popular science — which matters in a topic swamped by cognitive-bias self-help. **Difficulty:** Beginner | **Language:** English | **Price:** Free **Link:** https://www.callingbullshit.org/ **Tags:** data-literacy, misinformation, critical-thinking, statistics, data-visualization ### 3. Reproducible Research This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results. **Difficulty:** Beginner | **Language:** English | **Duration:** 4-9 hours/week | **Price:** Free **Link:** https://www.coursera.org/learn/reproducible-research **Tags:** courses, science-research, scientific-method ## Videos ### 1. Jon Kabat-Zinn Guided Mindfulness Meditation A 2007 Google talk in which Jon Kabat-Zinn explains what mindfulness is, the research behind MBSR, and how attention training affects stress and health, interspersed with short guided sitting practice and audience questions. **Difficulty:** Beginner | **Price:** Free **Link:** https://www.youtube.com/watch?v=3nwwKbM_vJc **Tags:** mindfulness, mbsr, stress-reduction, guided-meditation, attention-training ## Websites ### 1. Data Colada **Author:** Uri Simonsohn, Leif Nelson, Joseph Simmons Blog by the researchers behind p-curve and several high-profile data-fraud investigations, posting worked forensic analyses of published papers, p-hacking diagnostics, and critiques of meta-analytic and statistical practice. The only source here that shows bias detection being performed on real published papers rather than described. Each post is a case study in what a manipulated or p-hacked dataset actually looks like, which is knowledge no textbook transmits well. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://datacolada.org/ **Tags:** p-hacking, research-integrity, replication, statistics, meta-research ### 2. Cochrane Handbook Chapter 8: Assessing Risk of Bias in a Randomized Trial **Author:** Julian PT Higgins, Jelena Savović, Matthew J Page, Roy G Elbers, Jonathan AC Sterne The only resource here that operationalises bias assessment into a reproducible protocol rather than describing it. The Cochrane Handbook chapter specifying the RoB 2 tool: five domains of bias in randomised trials, signalling questions for each, and the algorithm that converts answers into low, high, or some-concerns judgements. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://www.cochrane.org/authors/handbooks-and-manuals/handbook/current/chapter-08 **Tags:** risk-of-bias, systematic-reviews, randomized-trials, evidence-based-medicine ### 3. Catalogue of Bias The Source Truth for the taxonomy of methodological bias — not a summary of one, but the canonical registry itself. Each entry follows a fixed structure (definition, example, impact, preventive steps), so it works as both a browsable primer and a working reference. An annotated catalogue from Oxford's Centre for Evidence-Based Medicine documenting biases that distort health research, with each entry giving a definition, the stage of research affected, published examples, and preventive steps. **Difficulty:** Beginner | **Language:** English | **Price:** Free **Link:** https://catalogofbias.org/ **Tags:** bias-taxonomy, evidence-based-medicine, research-methods, reference ### 4. cos.io Center for Open Science (cos.io) is a nonprofit that builds open-source tools and promotes reproducibility and transparency in research. The site explains and provides access to the Open Science Framework (OSF), preregistration, data sharing resources, and educational materials on replication and open science practices. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://cos.io **Tags:** websites, science-research, scientific-method ### 5. sciencebuddies.org Science Buddies is a nonprofit site offering hundreds of science project ideas, experiments, and step-by-step guides to help students plan investigations, formulate hypotheses, and present their findings. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://sciencebuddies.org **Tags:** websites, science-research, scientific-method ### 6. nature.com Nature.com is the official site of Nature journals, delivering peer‑reviewed research articles, reviews, and news across the natural sciences, including genetics and genetic engineering. It features content from Nature and related journals, plus expert commentary, features, and research highlights. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://nature.com **Tags:** websites, engineering-manufacturing, biotechnology ## Books ### 1. Causal Inference: What If **Author:** Miguel A. Hernán, James M. Robins Graduate textbook, free as a PDF, treating confounding, selection bias, and measurement bias as structural problems represented with causal diagrams rather than as statistical afterthoughts. Part I requires no modelling background. Chapters 7-9 (Confounding, Selection Bias, Measurement Bias) are the deepest treatment available of why these biases arise and what identification assumptions actually neutralise them — mental models, not procedures. **Difficulty:** Advanced | **Language:** English | **Price:** Free **Link:** https://miguelhernan.org/whatifbook **Tags:** causal-inference, confounding, selection-bias, causal-diagrams, epidemiology ## Papers ### 1. The Preregistration Revolution **Author:** Brian A. Nosek, Charles R. Ebersole, Alexander C. DeHaven, David T. Mellor Separates prediction from postdiction and sets out how preregistering hypotheses and analysis plans constrains selective reporting. Addresses nine practical objections, including protocol deviations, exploratory work, and preregistration of analyses on existing data. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://pmc.ncbi.nlm.nih.gov/articles/PMC5856500/ **Tags:** preregistration, open-science, research-methods, publication-bias ### 2. The Garden of Forking Paths **Author:** Andrew Gelman, Eric Loken Gelman and Loken argue that data-dependent analysis choices inflate false positives even when researchers never run multiple tests, because the analysis performed was contingent on the data. Explains why preregistration, not intent, is the fix. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://sites.stat.columbia.edu/gelman/research/unpublished/forking.pdf **Tags:** p-hacking, multiple-comparisons, research-methods, statistics ### 3. Why Most Published Research Findings Are False **Author:** John P. A. Ioannidis Ioannidis models research findings as a Bayesian positive predictive value problem, showing how low prior odds, small samples, weak effects, analytic flexibility, and competing teams make most published claims more likely false than true. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0020124 **Tags:** publication-bias, replication-crisis, false-positives, research-methods, meta-research --- *This content is part of Dantes.io - Your Treasure Map to Knowledge* *Curated by humans at Dantes.io. Personal study use welcome; republishing this curation requires permission (team@dantes.io).* View this page online: https://dantes.io/subject/bias-mitigation