From the article:

Counterintuitively, as the size of a study increases, the probability that its confidence intervals will include the truth decreases (1). Thus, studies with large sample sizes can yield misleadingly narrow confidence intervals around heavily biased results. Because large quantities of data are now available to researchers, this phenomenon has been labeled the “big data paradox” and is a major culprit behind the unreliable predictions made from the 2016 United States (US) general election polling datasets (2), Google Flu Trends (3), and Delphi–Facebook, i.e., the largest public health survey ever conducted in the US to date (4). All study types, including randomized controlled trials (RCTs) are susceptible to this paradox (5). We therefore need to understand why it occurs and how to best address it when interpreting research studies.