I don't think so. "Statistically significant" is a relative term and when testing an entire population is infeasible (as it often is), we instead sample some fraction that we believe is "statistically significant" on the assumption that it will accurately reflect the whole.
The point of this article is that a sample only accurately reflects the whole in some ways. Variability in particular scales with the square root of the sample size. And since misconceptions about variability have been at the heart of many controversies (male vs. female intelligence, school size, cancer risk, etc.), De Moivre's equation is important; even dangerous in the sense that ignorance of it has led to billions of dollars wasted.
The point of this article is that a sample only accurately reflects the whole in some ways. Variability in particular scales with the square root of the sample size. And since misconceptions about variability have been at the heart of many controversies (male vs. female intelligence, school size, cancer risk, etc.), De Moivre's equation is important; even dangerous in the sense that ignorance of it has led to billions of dollars wasted.