Source notes

A result labeled statistically significant can sound more conclusive than the evidence warrants. The statement explains what a p-value can and cannot establish. It describes how unusual a statistical result would be under a specified model and its assumptions. It does not give the probability that a scientific claim is true, measure the size of an effect or determine its practical importance. The familiar threshold of 0.05 does not separate truth from falsehood. The authors set out principles for interpretation and transparent reporting, rather than testing a health intervention or estimating its benefit.

The same estimated effect can produce different p-values when measurement precision or sample size differs. A small p-value therefore need not accompany a large or useful effect, and a larger value does not establish that nothing is happening. The statement also warns against selective reporting: choosing results because they cross a threshold can make the reported analysis misleading when the selection process remains hidden.

How Does This Show Up in Real Life?

Suppose a headline says a treatment, diet or exercise program produced a significant improvement. That wording leaves several practical questions unanswered. How large was the observed change? How uncertain is the estimate? Were the participants and measurements relevant to the claim? Were other analyses conducted but left out of the report? The statement encourages interpretation that includes those questions rather than ending at a statistical label. This does not mean that every small p-value is uninformative or that statistical analysis should be abandoned. It means that a number has a defined role within an argument. For a reader deciding how seriously to take a claim, the result needs to be considered alongside study design, assumptions, reporting and the surrounding evidence before it can support a practical conclusion.

This is guidance about reasoning and reporting, not a complete manual for choosing an analysis. It deliberately leaves some statistical controversies and technical issues unresolved. Alternative approaches also rely on assumptions. The statement cannot determine the validity of a particular study without its details, and it contains no intervention follow-up from which to infer lasting health effects.

Why We Cite It

Alpekin can use this source to guide the language used when translating a study into public-facing text. It provides a basis for avoiding a change in claim strength merely because a statistical threshold was crossed, and for keeping numerical evidence connected to the question the research was designed to address.

Take the statement as a standard for careful interpretation. Its value is in improving the questions asked of evidence, not replacing one automatic rule with another. A scientific conclusion needs a reasoned account of the study, rather than a statistical label alone.