Effect estimation, rather than relying on p-values, is one approach that provides far more context than just "Is or is not significant". Also training your sci

Effect estimation, rather than relying on p-values, is one approach that provides far more context than just "Is or is not significant". Also training your scientists - especially those outside the physical sciences - that effects likely aren't fixed in a meaningful sense (i.e. the effect of smoking on lung cancer isn't a universal constant in the way the speed of light is), at which point multiple estimates of the same effect from different groups and populations has value.