Why 'No Significant Result' Is Not the Same as 'No Effect' — And How That Shapes What Journals Publish

Public trust in science depends on understanding how evidence is produced and interpreted. Longstanding statistical debates have resurfaced amid concerns about trust in research, and they can easily confuse readers who encounter headlines or social posts about new studies.

I have worked as a statistician for many years and have seen how careful design and transparent reporting shape reliable conclusions. Below I explain the journey from an initial question to publication, and why some results make headlines while others quietly disappear.

From hypothesis to decision

Scientists typically begin with a hypothesis — a testable claim. For example: do certain BRCA gene mutations increase breast cancer risk? They collect data and then evaluate the evidence. It is tempting to treat that evaluation as a simple true-or-false judgment, but statistical inference is more nuanced.

Type I and Type II errors

If a study concludes a false claim is true, that is a false positive (Type I error). If it fails to detect a real effect, that is a false negative (Type II error). Most statistical procedures explicitly control the Type I error rate, which makes avoiding false positives a priority — but that control can come at the expense of higher Type II error rates.

Null hypothesis significance testing (NHST)

Under NHST, researchers set up a null hypothesis that typically contradicts the claim they hope to support (for example, “BRCA mutations are not associated with higher breast cancer risk”). After gathering data, they ask whether there is enough evidence to reject the null. Rejecting the null is treated practically as evidence for the claim; failing to reject the null is not evidence that the null is true — it is an inconclusive result.

This asymmetry matters. NHST is designed to limit false positives at a chosen significance level, but it provides weaker guarantees about false negatives. The study's ability to detect a real effect — its power — depends on sample size, study design and the true effect size. Large, clear effects are easy to detect; small effects are often missed.

Publication and positive publication bias

When researchers submit results to peer-reviewed journals, editors and reviewers decide what to publish. Editors naturally prefer studies that report clear, novel findings — those that reject their null hypotheses — because they tend to seem more informative and newsworthy. This editorial preference is called positive publication bias.

Labeling this bias as simply “bad” misses nuance. Given NHST’s structure, studies that merely fail to reject the null are legitimately inconclusive; they do not provide the affirmative evidence most journals and readers seek. That said, when researchers manipulate analyses, selectively report outcomes, or engage in questionable practices to produce significant p-values, false-positive rates rise — and editorial preference can amplify that problem.

How to show 'no meaningful effect'

Researchers can design studies so that the hypothesis they care about is framed in a way that rejecting the null supports a negative claim. Equivalence or non-inferiority tests, and pre-specifying a smallest meaningful effect size, allow investigators to gather evidence that an effect is small enough to be practically negligible. This approach aligns study goals with the logic of hypothesis testing.

Practical guidance for readers

• Non-significant ≠ evidence of absence. Look for confidence intervals and whether they rule out meaningful effects.
• Pay attention to effect sizes and sample sizes, not only p-values.
• Value replication, preregistration and transparent reporting — these reduce the risk of false positives.
• Understand whether a study used equivalence or non-inferiority tests when it claims “no effect.”

In short, knowing how hypothesis testing and publication practices work helps explain why certain studies are more visible and why “no significant result” should be interpreted cautiously. Sound study design, transparent methods and responsible reporting are essential to improving what is published and how it is understood.

Author: Mark Louie Ramos, Penn State.

Disclosure: The author reports no relevant financial conflicts.