The Field Guide

How to read science news

Short, plain explainers for the words and warning signs that decide how much a study is really worth. One page each, so you can tell a real breakthrough from a hopeful headline.

01 Clinical trial phases Phase 1 tests whether a drug is safe, phase 2 tests whether it works, and phase 3 confirms both in a large group before approval.
02 Animal model translation A result in mice is an early clue, not a cure, because most treatments that work in animals never work in people.
03 Effect size Effect size measures how big a difference is, which is the question that decides whether a real result actually matters in your life.
04 Relative and absolute risk A "50% risk cut" is relative; the absolute change can be tiny, so always ask what the risk went from and to in real numbers.
05 Preprint and peer review A preprint is a study posted before independent experts have vetted it, while peer review is that vetting step, so preprints deserve extra caution.
06 Correlation and causation Two things happening together does not prove one causes the other, so "linked to" headlines rarely establish cause and effect.
07 Reproducibility and replicability A finding earns trust when other scientists can repeat it, so one study is a data point, not a settled conclusion.
08 P-value A p-value measures how surprising the data would be if there were no real effect, not the probability that a finding is true.
09 Confidence interval A confidence interval is the plausible range around a result, which tells you how precise, or shaky, that single reported number really is.
10 Randomized and observational studies A randomized trial assigns people to groups to test cause, while an observational study only watches, which is why trials give stronger evidence.
11 Meta-analysis A meta-analysis pools many separate studies into one combined estimate, which usually beats any single study, as long as the studies it pools are sound.
12 Survivorship bias Survivorship bias is drawing conclusions from the things that made it through while ignoring the ones that did not, which quietly warps the picture.
13 Regression to the mean Regression to the mean is the tendency for an unusually high or low measurement to be closer to average the next time, which can make a treatment look like it worked when nothing did.
14 Nutritional epidemiology Diet headlines reverse so often because most rest on observational studies with tiny effects and countless confounders, so the same food gets linked to both harm and benefit.
15 Number needed to treat The number needed to treat is how many people have to take a treatment for one of them to benefit, which is often the plainest measure of how much a drug really does.
16 Publication bias Studies that find a positive result are more likely to be published than those that find nothing, so the literature you can see overstates how real and how big an effect is.
17 Funding effect (industry sponsorship bias) Studies funded by the company that sells the product tend to come out more favorably, a pattern strong enough that who paid for the research is part of how you should read it.
18 Lead-time bias Detecting a disease earlier automatically lengthens 'survival since diagnosis' even when it does not delay death by a single day, which makes screening look more effective than it is.
19 Healthy-user bias People who take a preventive treatment or follow health advice are, on average, already healthier and more careful, so their better outcomes can be mistaken for the effect of the treatment.
20 Composite endpoint A composite endpoint bundles several separate outcomes into one, which makes a trial smaller and faster but can blur together events of very different importance.

Every quoted line is verbatim from the source linked on its page. The explanations are ours, written plainly, so you can read a study for what it is worth without a statistics degree.

The best filter is a daily habit.