Research Literacy

Start Here

New to evidence quality: Start with How to Evaluate Longevity Evidence.
Need to compare study designs: Read Randomized Controlled Trials vs Observational Studies in Longevity Research.
Need to separate association from causation: Read Correlation, Confounding, and Causation in Longevity Research.
Need to understand when the outcome may be changing the exposure: Read Reverse Causation in Ageing and Longevity Research.
Need to distinguish review types: Read Systematic Reviews vs Narrative Reviews in Longevity Research.
Need to distinguish rerunning a result from independently confirming it: Read Replicability vs Reproducibility in Longevity Research.
Need to understand why small studies can mislead: Read Sample Size, Statistical Power, and Why Small Studies Mislead.
Need to interpret p-values and confidence intervals: Read P-Values, Confidence Intervals, and Statistical vs Practical Significance.
Need to understand combined outcomes like death, dementia, and disability: Read Composite Endpoints in Ageing Research.
Need to interpret headlines and risk: Read How to Interpret Relative Risk vs Absolute Risk.
Need to spot hype in media coverage or press releases: Read How to Spot Overclaiming in Longevity Headlines and Press Releases.
Wondering why positive results dominate coverage: Read Publication Bias and Why Positive Longevity Results Get More Attention.
Need to understand why healthier participants can skew results: Read Selection Bias and Healthy User Bias in Longevity Studies.
Want to read papers better: Go to How to Read a Longevity Study Abstract.
Need to inspect methods and caveats: Read How to Read the Methods and Limitations of a Longevity Study.
Need source-finding help: Start with Where to Find Reliable Longevity Research.

What This Section Covers

Study design: how observational studies, trials, systematic reviews, narrative reviews, and preprints differ.
Causality and risk: how to separate association, relative risk, and practical importance.
Source quality: where to find stronger research and how to assess reliability.
Biomarker evidence: what makes a biomarker study credible rather than superficial.

Where to Start for Specific Questions

Question	Best Page to Start With	Why
How do I judge whether a longevity claim is strong?	How to Evaluate Longevity Evidence	Gives the broadest framework for study quality, causality, and overinterpretation
What is the difference between reproducibility and replication?	Replicability vs Reproducibility in Longevity Research	Explains the difference between rerunning the same analysis and confirming a finding in new data or settings
How much should I trust a very small study?	Sample Size, Statistical Power, and Why Small Studies Mislead	Explains why low power can produce both missed effects and exaggerated positive findings
What does “statistically significant” actually mean?	P-Values, Confidence Intervals, and Statistical vs Practical Significance	Explains what p-values and confidence intervals do and do not tell you, and why significance is not the same as importance
How should I read a study that combines death, dementia, disability, or disease events?	Composite Endpoints in Ageing Research	Explains why combined outcomes are used in ageing trials and how they can mislead if one component drives the result
How should I interpret risk numbers in headlines?	How to Interpret Relative Risk vs Absolute Risk	Explains why effect size language often misleads readers
How do I tell when a headline or press release is stronger than the study?	How to Spot Overclaiming in Longevity Headlines and Press Releases	Shows the main warning signs: causal overstatement, biomarker inflation, and over-translation from preclinical work
How do I tell whether a longevity association is actually causal?	Correlation, Confounding, and Causation in Longevity Research	Explains why observational associations can be distorted by confounding and what strengthens causal interpretation
How do I tell whether the outcome may already be altering the exposure?	Reverse Causation in Ageing and Longevity Research	Explains why prodromal disease, frailty, and terminal decline can make a predictor look causal when it is partly downstream of the outcome process
Should I trust a systematic review more than a narrative review?	Systematic Reviews vs Narrative Reviews in Longevity Research	Explains what each review type can and cannot reliably tell you
When should I trust a randomized trial more than an observational study?	Randomized Controlled Trials vs Observational Studies in Longevity Research	Explains the tradeoff between causal clarity, long follow-up, and real-world applicability
Why do positive longevity findings seem to get more attention?	Publication Bias and Why Positive Longevity Results Get More Attention	Explains how selective publication, reporting, and media amplification can skew the visible evidence
How can healthier participants distort a longevity study?	Selection Bias and Healthy User Bias in Longevity Studies	Explains how volunteer effects, healthy user differences, and selective retention can exaggerate associations
How do I read a study abstract without overreading it?	How to Read a Longevity Study Abstract	Shows what the abstract reveals and what it often leaves out
How do I judge the methods and limitations section of a paper?	How to Read the Methods and Limitations of a Longevity Study	Explains how design, endpoints, bias control, and stated caveats shape interpretation
Where should I look for more reliable longevity research?	Where to Find Reliable Longevity Research	Points readers toward stronger source types and search habits
What makes a biomarker paper strong or weak?	What Counts as a Good Biomarker Study?	Focuses on the specific validation and interpretation problems in biomarker research

Why This Matters

Longevity science is complex and frequently misrepresented in media headlines. Understanding how to evaluate the quality of evidence is essential for navigating this field.

Interpreting Study Quality

Not all scientific studies are created equal. In evidence-based medicine, we rank studies based on their reliability:

Systematic Reviews & Meta-Analyses: These studies pool data from multiple smaller studies to find robust patterns. They are generally considered the highest level of evidence.
Randomized Controlled Trials (RCTs): The "gold standard" for testing interventions. Participants are randomly assigned to a treatment or control group to minimize bias.
Observational Studies: Researchers observe outcomes without intervening. These can find associations but cannot prove causation.
Animal & Cell Studies: Essential for early research but often fail to translate to humans.

Correlation vs. Causation

A classic pitfall in reading health news is confusing correlation with causation. Just because two things occur together does not mean one causes the other.

Example: A study might find that people who drink green tea live longer.
Correlation: Green tea drinkers have higher longevity.
Possible Confounders: Perhaps green tea drinkers also smoke less, exercise more, or have higher incomes. Without controlling for these factors, we cannot say the tea caused the longer life.

Why Headlines Oversimplify

News outlets often favor sensationalism ("Cure for Cancer Found!") over nuance ("Compound X showed promise in mice"). At Starlight Longevity, we strive to include the missing context: Was it in humans? What was the sample size? Has it been replicated?

Reading Studies

Interpreting Claims

Finding Sources

Where to Find Reliable Longevity Research