From      What is true is that trivially small effects can be found with very large sample sizes. That does not suggest that you shouldn't have such large sample sizes. What it means is that the way you interpret your finding is dependent upon the effect size and sensitivity of the test. If you have a very small effect size and highly sensitive test you have to recognize that the statistically significant finding may not be meaningful or useful.   Why "If you have a very small effect size and highly sensitive test you have to recognize that the statistically significant finding may not be meaningful or useful"?
In a  of Amstat News, the authors (Mark van der Laan and Sherri Rose) stated that "We know that for large enough sample sizes, every study—including ones in which the null hypothesis of no effect is true — will declare a statistically significant effect.".  Well, I for one didn't know that. Is this true? Does it mean that hypothesis testing is worthless for large data sets?
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