Blood transfusions are an essential component of modern-day medicine, saving lives in a variety of situations, ranging from genetic diseases like sickle cell anemia to road accidents. But, the history of blood transfusion is a rocky one. For instance, did you know that a German physician founded the world’s first blood transfusion institute in 1926 because he believed blood transfusions led to immortality?

Dr. Alexander Bogdanov started some crude blood transfusion experiments on himself by injecting blood of other young men into his own system. After 11 such transfusion sessions, he claimed to have improved vision, arrest hair loss, and produce youthful skin. This led him to believe that blood transfusion was the path to immortality and eternal youth. But, as you can expect, this practice, combined with poor understanding of the blood transfusion process at that time, killed him years later.

Kiran Musunuru was shocked. In a few days, on Nov. 27, 2018, scientists from all over the world would meet in Hong Kong to set standards for the use of the CRISPR gene-editing tool on human embryos. Yet the paper in front of him suggested that in China, gene-edited twins were already growing in their mother’s uterus, with the help of scientist He Jiankui.

“I was horrified,” Musunuru recalled as he spoke to science writers gathered in State College, Pa. 11 months later, for the ScienceWriters2019 conference. “This is an historic event, the first gene-edited babies. And this is a horror show.”

That day, Associated Press reporter Marilynn Marchione had requested the opinion of three experts in genetics on an unpublished paper. Musunuru, an associate professor at the University of Pennsylvania School of Medicine, was one of them. The claims made by He, the paper’s lead author, were grandiose and terrifying: he had implanted gene-edited embryos.

When statistician Nicole Lazar published an editorial in The American Statistician earlier this year advocating changes in the way scientists handle the troublesome issue of statistical significance, her father—who trained as a sociologist—asked her, "Are you getting death threats on Twitter?"

Lazar, a professor of statistics at the University of Georgia, doesn't use Twitter, but the question reveals how contentious the issue of statistical significance is. "You don't often think about statisticians getting emotional about things," Lazar told an audience of writers attending the Science Writers 2019 conference held in State College, Pa.,"but this is a topic that's been raising a lot of passion and discussion in our field.” Lazar spoke on Oct. 27 as part of the New Horizons in Science briefing organized by the Council for the Advancement of Science Writing (CASW).

Many scientists determine whether the results of their experiments are “statistically significant” by using statistical tests that result in a number known as the “p-value.” A p-value of less than 0.05 is commonly considered significant, and often erroneously characterized as meaning the findings are not likely to be the result of chance. What the number actually reveals is less straightforward, and even scientists have trouble explaining the precise meaning of the p-value. Using the threshold of p < 0.05 has been shown to be problematic, misleading, and even dangerous. Lazar’s editorial, “Moving to a World Beyond 'p < 0.05',” discusses several possibilities that will give researchers alternatives to an arbitrary p-value cut-off.