Why doesn't Gorland cite specific journals?
We write for a general audience and summarize broadly understood concepts rather than adjudicating individual academic papers, which is outside the scope of an editorial publication.
Reading research
How to read a training claim
A practical guide to study design, context, uncertainty, and the questions worth asking before adopting a new idea about training or recovery.
Numbers need a question
A measurement is useful when it answers a defined question. Consider who was studied, how long the observation lasted, what was compared, and whether the result matters in ordinary life rather than only in a laboratory setting. A finding about highly trained athletes in their twenties, for example, may describe a different pattern than what applies to an active 45-year-old returning to structured training after a break.
It also helps to ask what the comparison group was doing. A result described as "improved endurance" means little without knowing whether it was compared against rest, against a different training style, or against no change at all.
A careful reading sequence
- Identify the population and setting — age range, activity level, and whether the context resembles your own.
- Separate association from causation — two things changing together does not mean one caused the other.
- Look for size, duration, and practical relevance — a statistically notable change may still be too small to matter day to day.
- Compare the claim with other credible, general-interest sources rather than relying on a single headline.
This sequence is not specific to exercise science; it applies to nutrition claims, recovery tools, and most popular health reporting. Treating it as a habit rather than a one-time checklist makes it far more useful over time.
Common patterns that deserve a second look
| Pattern | Why it deserves caution | A more useful question |
|---|---|---|
| Small sample, strong headline | A result from a handful of participants is more sensitive to individual variation | How many people were actually studied? |
| Short observation window | Early changes do not always persist over months | Did the effect hold beyond the first few weeks? |
| Single-study claim | One study rarely settles a complex physiological question | Do other general sources describe a similar pattern? |
| Outcome measured indirectly | A proxy measure may not reflect the outcome a reader actually cares about | Is the reported measure the one that matters to me? |
A short glossary for reading research
- Sample size
- The number of participants in a study; smaller samples carry more uncertainty.
- Control group
- A comparison group that does not receive the intervention being studied, used to judge what would have happened anyway.
- Statistical significance
- A measure of whether a result is unlikely to be due to chance alone — not a measure of how large or meaningful the effect is.
- Effect size
- A description of how large a measured change actually is, separate from whether it is statistically significant.
- Replication
- Whether other independent studies have found a similar result under similar conditions.
“A single study is a data point, not a verdict.”
“Ask what was measured before asking what it means.”
“Context changes conclusions more than headlines suggest.”
“Uncertainty is information, not a weakness in the writing.”
Can I ask about a specific claim I read elsewhere?
Yes — send it through the contact page. We cannot evaluate every external claim, but we are glad to point toward a reasonable way to think about it.
Does this page replace expert consultation?
No. It is a reading habit, not a substitute for a qualified professional's assessment of your specific situation.