How to Write Annotated Bibliographies
Master the art of summarizing, evaluating, and connecting your sources
Need stronger vocabulary for your evaluation sentences? Browse nuanced word choices in WordLibrary to keep annotations precise and engaging.
What is an Annotated Bibliography?
An annotated bibliography is a list of sources (books, articles, websites) with a paragraph under each citation that describes and evaluates the source. Think of it as your citation plus your thoughts about why the source matters.
Formula:
Citation + Summary + Evaluation + Relevance = Annotation
The Three Essential Parts
1️⃣ Summary (What does this source say?)
Briefly describe the main argument or findings. Answer: What is this source about?
Sentence Starters:
- • "This article examines..."
- • "The author argues that..."
- • "The study investigates..."
- • "This source explores..."
2️⃣ Evaluation (How credible is it?)
Assess the source's quality and reliability. Answer: Can I trust this source?
Questions to Ask:
- • Is the author an expert in this field?
- • Is it peer-reviewed or from a reputable publisher?
- • Does it cite credible sources?
- • Is it current enough for my topic?
- • Are there any obvious biases?
3️⃣ Relevance (How does it relate to your research?)
Explain how this source fits into your project. Answer: Why am I using this?
Connection Phrases:
- • "This source will help me explain..."
- • "I will use this to support my argument that..."
- • "This provides background on..."
- • "This challenges my assumption that..."
Complete Example (APA)
Martinez, J. R. (2022). Climate migration and human rights. University of California Press.
Summary (Blue)
This book examines how climate change forces people to migrate from their homes and the human rights implications of this displacement. Martinez analyzes case studies from Pacific island nations and coastal communities in Southeast Asia.
Evaluation (Green)
Martinez is a professor of environmental law at UC Berkeley with 20+ publications on climate policy. The book is published by a university press and includes extensive citations to peer-reviewed research. It provides a balanced view of both environmental and legal perspectives.
Relevance (Amber)
This source will help me explain the legal challenges facing climate refugees in my paper. The case studies provide specific examples I can use to support my argument that current international law is inadequate to protect climate migrants.
Full Annotation (150-200 words):
This book examines how climate change forces people to migrate from their homes and the human rights implications of this displacement. Martinez analyzes case studies from Pacific island nations and coastal communities in Southeast Asia. Martinez is a professor of environmental law at UC Berkeley with 20+ publications on climate policy. The book is published by a university press and includes extensive citations to peer-reviewed research. It provides a balanced view of both environmental and legal perspectives. This source will help me explain the legal challenges facing climate refugees in my paper. The case studies provide specific examples I can use to support my argument that current international law is inadequate to protect climate migrants.
Writing Tips
Keep it concise
Aim for 150-200 words. One paragraph is usually perfect.
Use your own words
Don't copy from the source's abstract. Paraphrase and analyze.
Follow the format
Citation first, then annotation paragraph below it (not indented).
Be specific
Instead of "This is a good source," explain what makes it valuable.
Alphabetize your entries
Just like a regular bibliography, order sources alphabetically by author.
Common Mistakes to Avoid
Copying the abstract
Write your own summary in your own words.
Being too vague
"This is a good source" doesn't tell your professor anything useful.
Skipping evaluation
Always assess credibility—it shows critical thinking.
Writing in first person excessively
One or two "I will use..." is fine, but keep focus on the source.
Word Count Breakdown
Total: 150-200 words per annotation
Examples by Source Type
See how annotations differ based on the type of source you're citing.
Journal Article
This peer-reviewed study follows 500 teenagers over two years to examine correlations between social media use and anxiety/depression symptoms. Chen and Rodriguez found that teens spending 3+ hours daily on social platforms showed significantly higher anxiety scores. Both authors are psychology professors at Stanford with expertise in adolescent development. The study uses robust methodology with validated assessment tools and controls for confounding variables. This source provides the statistical evidence I need to support my thesis that excessive social media use harms teen mental health. The longitudinal design strengthens causal claims compared to cross-sectional studies.
Book
Anderson examines the business models of major tech companies and how they design products to maximize user engagement through psychological manipulation. The book covers variable reward systems, infinite scroll, and notification triggers. Anderson is a former Google designer turned critic, bringing insider perspective to his analysis. Published by MIT Press, the book includes extensive interviews with industry insiders and cites peer-reviewed research on behavioral psychology. This source will help me explain the economic incentives behind addictive app design in my paper. The insider perspective adds credibility that academic studies alone cannot provide.
Website
This CDC report presents national data on teen health behaviors including screen time, sleep patterns, and mental health indicators. The 2023 survey includes responses from 17,000 high school students across all 50 states. The CDC is the authoritative government source for health statistics, and their surveys use validated instruments with rigorous sampling methodology. The data is current and representative of the U.S. teen population. I will use these statistics to establish baseline prevalence rates of teen mental health issues and demonstrate how they correlate with technology use patterns. The large sample size and government credibility make this an essential foundational source.
News Article
This investigative report reveals that Instagram's internal research found their platform increased anxiety and body image issues among teenage girls, but the company downplayed these findings publicly. Thompson cites leaked internal documents and interviews with former employees. While this is journalism rather than peer-reviewed research, The New York Times has rigorous fact-checking standards, and the article cites verifiable sources. The limitation is that it reports on unpublished internal studies, so methodology cannot be fully evaluated. I will use this source to discuss the gap between what tech companies know about their products' harms versus what they disclose, adding an ethical dimension to my argument about platform accountability.
Good vs. Bad Annotations: Learn What Works
Compare these examples to understand what makes an annotation effective.
Bad Annotation (Too Vague)
This book talks about climate change and how it affects the environment. The author is an expert and the book has good information. It's a credible source because it's published by a real publisher. I will use this source in my research paper about climate change.
What's Wrong:
- • Too vague - "talks about" doesn't explain what the book argues
- • No specific evaluation - what makes the author an expert?
- • Generic relevance - doesn't explain HOW you'll use it
- • Missing key details - no specific findings or arguments
Good Annotation (Specific & Clear)
Smith argues that rising sea temperatures are causing irreversible damage to coral reef ecosystems, with 30% of reefs experiencing severe bleaching events annually. The book presents case studies from the Great Barrier Reef and Caribbean coral systems, documenting species loss and ecosystem collapse. Smith is a marine biologist with 25 years of field research and over 50 peer-reviewed publications on coral ecology. The book is published by an academic press and includes extensive citations to current research. I will use Smith's coral bleaching data to demonstrate the cascading effects of climate change on marine food chains in my paper's environmental impact section.
What's Good:
- • Specific argument with data (30% of reefs, bleaching events)
- • Concrete credibility (25 years experience, 50+ publications)
- • Clear relevance (coral bleaching data for food chain argument)
- • Precise details about methodology and scope
Bad Annotation (Missing Evaluation)
This article examines how social media platforms affect democratic participation and political discourse. Johnson discusses how algorithms create echo chambers and filter bubbles that reinforce existing beliefs. The study looks at Twitter, Facebook, and TikTok usage patterns among voters during the 2020 election. The article also covers misinformation spread and polarization trends across different age groups and political affiliations.
What's Wrong:
- • Only summarizes - no evaluation of credibility or quality
- • No relevance section - doesn't explain how you'll use it
- • Reads like an abstract copy - needs critical analysis
- • Missing the "why this matters" connection to your research
Good Annotation (All Three Parts)
Johnson examines how social media algorithms create echo chambers that polarize political discourse, analyzing Twitter, Facebook, and TikTok usage patterns among 10,000 voters during the 2020 election. The study found that 78% of users primarily encounter content reinforcing their existing political views. Johnson is a political science professor at Georgetown with expertise in digital democracy, and this article appears in a peer-reviewed journal with rigorous editorial standards. The study's large sample size and multi-platform approach make it methodologically strong, though it's limited to U.S. voters. I will use Johnson's echo chamber data to support my argument that social media platforms undermine democratic deliberation by reducing exposure to diverse political perspectives.
What's Good:
- • Summary with specific data (10,000 voters, 78% statistic)
- • Evaluation of credentials and methodology strengths/limits
- • Relevance explains exactly how you'll use the data
- • Demonstrates critical thinking about the source
Bad Annotation (Too Long & Unfocused)
This comprehensive book explores every aspect of artificial intelligence applications in modern healthcare systems. Williams begins with a history of computing in medicine dating back to the 1960s, then covers machine learning algorithms, neural networks, natural language processing, computer vision for radiology, robotic surgery systems, drug discovery platforms, patient monitoring devices, electronic health records, telemedicine platforms, and predictive analytics for hospital management. Each chapter includes case studies from major hospitals and interviews with doctors, nurses, and IT administrators. The author discusses ethical concerns about AI bias, data privacy issues under HIPAA regulations, and the future of human-AI collaboration in clinical settings. Williams worked as a software engineer at IBM Watson Health for 10 years and has published three previous books on technology in medicine. The book is 450 pages long and includes over 300 citations to research papers and industry reports. I found this book through my university library database and it seems very thorough and well-researched with lots of information I might be able to use somewhere in my paper about healthcare technology, though I'm not exactly sure where yet since it covers so many different topics.
What's Wrong:
- • Way too long (250+ words) - should be 150-200 words
- • Lists everything instead of focusing on relevant parts
- • Vague relevance ("might use somewhere") shows no focus
- • Too much unnecessary detail about how you found it
Good Annotation (Focused & Concise)
Williams examines AI diagnostic tools in radiology and pathology, focusing on how machine learning algorithms detect cancer in medical images with 95% accuracy—matching or exceeding human radiologists. The book includes case studies from Mayo Clinic and Johns Hopkins showing reduced diagnostic errors and faster detection times. Williams brings practical expertise from his 10-year tenure at IBM Watson Health and cites over 100 peer-reviewed studies on AI diagnostic performance. However, the book focuses primarily on success stories and gives limited attention to implementation challenges in smaller healthcare systems. I will use Williams' accuracy data and Mayo Clinic case study to demonstrate AI's potential to reduce diagnostic errors in my paper's technology solutions section, while acknowledging the implementation barriers he mentions.
What's Good:
- • Right length (170 words) - complete but concise
- • Focuses only on the parts relevant to the research project
- • Includes both strengths and limitations (balanced evaluation)
- • Clear, specific relevance to a particular paper section