This picture has the same interpretation, but notice the “significant” negative effects are more or less balanced by the “significant” positive effects. With one very large negative effect at the bottom.

If statistical modelling was objective, and if statistical practice and theory worked as advertised, all results should be the same, for both analysis, with only small differences. Yet the differences are many and large, as they were with Breznau; therefore, statistical practice is not objective, and statistical theory is deeply flawed.

There are many niceties in Gould’s paper about how all those analysts carried out their models, with complexities about “fixed” versus “random” effects, “independent” versus “dependent” variables, variable selection and so forth, even out-of-sample predictions, which will be of interest to statisticians. But only to depress them, one hopes, because *none* of these things made any difference to the outcome that researchers disagreed wildly on simple, well defined analysis questions.

The clever twist with Gould’s paper was that all the analyses were peer reviewed “by at least two other participating analysts; a level of scrutiny consistent with standard pre-publication peer review.” Some analyses came back marked “unpublishable”, other reviewers demanded major or minor revisions, and some said publish-as-is.

Yet the peer-review process, like details about modeling, *made no difference either*. The disagreements between analysts’ results was the same, regardless of peer-review decision, and regardless of modeling strategies. This is yet more evidence that peer review, as we have claimed many times, is of almost no use and should be abandoned.

If you did not believe Science was Broken, you ought to now. For both Breznau and Gould prove that you must not trust *any* research that is statistical in nature. This does not mean all research is wrong, but it does mean that there’s an excellent chance that if a study in which you take an interest were to be repeated by different analysts, the results could change, even dramatically. The results could even come back with an opposite conclusion.