Why your favorite scientific study might be hiding a massive margin of error

Ideas & Philosophy · Published 3 October 2026 Swipe more like this →

In short: Jerzy Neyman, a Polish-born mathematician, revolutionized how we measure uncertainty. By developing the confidence interval in the 1930s, he gave scientists a rigorous way to quantify the reliability of their data. Yet, even today, these intervals are frequently misunderstood by the very researchers who rely on them for truth.

Born in 1894, Jerzy Neyman navigated a life defined by the turbulence of the Russian Revolution and two world wars. After studying at Kharkov University, where he was deeply influenced by the work of Henri Lebesgue and Aleksandr Bernstein, Neyman eventually moved to Poland and later London. It was during his time working with Egon Pearson that he began to reshape the foundations of modern statistics, moving away from older, less rigorous methods.

Neyman’s most enduring contribution, the confidence interval, emerged in the 1930s as a method for statistical estimation. A confidence interval provides a range of values—an upper and lower bound—within which a population parameter is expected to fall. For instance, if researchers want to estimate the average weight of fish in a lake, they calculate a sample mean and apply a margin of error based on a chosen confidence level, such as 95 percent. This level represents the reliability of the process itself: if the experiment were repeated many times, 95 percent of the resulting intervals would capture the true population parameter.

Despite their utility, these intervals are widely misinterpreted. A common error is the belief that a 95 percent confidence interval implies a 95 percent probability that the true parameter lies within that specific range. In reality, the interval reflects the long-term reliability of the estimation process, not the probability of a single result. This confusion is not limited to the public; studies show that even professional scientists often struggle with these nuances. It was not until the 1980s—roughly 50 years after Neyman’s invention—that medical journals began mandating the use of confidence intervals, finally forcing a broader adoption of his rigorous approach to uncertainty.

Neyman’s influence extended far beyond theory. His work on stratified sampling provided the methodological backbone for modern polling, including the Gallup Poll. By the time he received the National Medal of Science in 1968, he had established himself as a principal founder of modern theoretical statistics, leaving a legacy that continues to define how we interpret data in medicine, meteorology, and beyond.

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