Social sciences have tended to over-emphasise null hypothesis significance testing and under-emphasise use of effect sizes:
"I believe that the almost universal reliance on merely refuting the null hypothesis as the standard method for corroborating substantive theories in the soft areas is a terrible mistake, is basically unsound, poor scientific strategy, and one of the worst things that ever happened in the history of psychology" (Meehl, 1978, p. 817)
Statistical significance is a function of effect size, sample size, and probability level - but most often, people want to know about the effect size (i.e., "how strong is the relationship or how big is the difference?).
Effect sizes can be re-expressed as other effect sizes or other common language formats such as percentages.
In applied social science research, people generally want to know about the strength of relationship or the size of the difference - so, report effect sizes. Don't rely solely on inferential statistical testing.
Graph effect sizes and include confidence intervals.