The tempting conclusion
Imagine a fictional study reporting an average improvement of four points in a large subgroup and three points in a smaller subgroup. The first result meets a significance cutoff; the second does not. A headline announces that the intervention works only in the first group.
That headline substitutes two separate questions for the one it claims to answer. Each subgroup test compares its own effect with a null value. Neither alone tests whether four points differs convincingly from three.
Put the actual contrast on the page
The estimated difference between subgroup effects is one point. Its uncertainty depends on the study design and analysis. Without that comparison, the difference in significance labels is insufficient evidence of an interaction.
Cochrane's meta-analysis guidance explicitly warns against comparing subgroup p-values in this way. It also cautions that subgroup analyses can be misleading, especially when many are explored after results are known.
A more informative reading order
First look at the effect estimate for each subgroup. Then look for a direct interaction test or interval. Next ask whether the subgroup question was specified in advance and whether there is a plausible reason to expect the difference.
A plausible explanation is not confirmation. With enough subdivisions, some apparent differences can arise by chance. Conversely, an imprecise interaction estimate does not prove the effects are identical.
What remains useful
Subgroup results can generate questions worth studying, show who was included, or expose an important gap in precision. Their value does not depend on announcing a decisive winner. “The smaller subgroup leaves more uncertainty” may be the most accurate and practically useful conclusion the data allow.
