p-hacking
Selectively reporting whichever analysis returned p < 0.05.
What it is
p-hacking is the practice of running many different analyses on the same data — different subgroups, different outcomes, different cut points, different covariates — and reporting only the ones that came back statistically significant. The result looks like a clean finding; the underlying truth is that with enough freedom, randomly noisy data will produce 'significant' results.
Why a reviewer cares
Reviewers look for tells: p-values that cluster suspiciously close to 0.05; long lists of covariates with one or two starred; cell-by-cell comparisons in heat-maps; oddly specific subgroup analyses presented as primary findings. A pre-registration that names the primary analysis blocks p-hacking — its absence is itself a flag.
How to fix it
Pre-register your primary analysis and report it as primary, then label additional analyses as exploratory. Apply a multiple-comparisons correction. Show the full set of analyses you ran, not just the winners — a 'multiverse analysis' (Steegen et al., 2016) systematically reports the effect under many reasonable analytical choices and lets the reviewer see how robust your conclusion is.
This is one of ~15 canonical methodology explainers Paper Review's red-team report links to. To get a full review of your manuscript, start a Paper Review — $5.