Something I learned early in science (and the same applies in all genuine scholarship) is that consensus is not truth; indeed, most often, the consensus is sure to be wrong. The same, and for ultimately the same reason, applies to the use of statistics.
When individual scientists disagree this is likely to be because they differ in things like ability, motivation, knowledge and honesty. The scientist most-likely to be correct in the one that excels in such characteristics. By taking a consensus of scholarship what is actually happening is that the best information is being obscured by worse information.
This can be seen in statistics, which is based upon averaging. Averaging takes the best data points and weights them with worse data points: data lower in (some dimension of) quality.
For example, in the egregious technique of meta-analysis, if there happen to be any really good studies (eg conducted by scientists that excel in ability, motivation, knowledge and honesty etc) then these will be combined with worse studies that will surely impair, obscure or perhaps even reverse their conclusions.
The correct mode of scholarship is to evaluate the work of each scholar (including each scientist) as a qualitatively distinct unit. Anything which obscures or over-rides this fact is a corruption - whether that is some consensus mechanisms, or a 'consensus of data-points' - i.e. statistics.
See also: https://charltonteaching.blogspot.co.uk/2010/10/scope-and-nature-of-epidemiology.html and its references
Note: Consensus and statistics alike have become dominant in research ("science") as the subject first professionalised, then expanded its personnel a-hundredfold; partly because modern "scientists" areby-now merely careerist bureaucrats: wrongly-motivated, incompetent and dishonest, who know-no-better (and care less). And partly because by such means the 99% non-real-scientists are thereby able to participate in the process, instead of being utterly ignored and irrelevant - as they deserve.