Unscientific method: Study finds all those big data studies are mostly big mistakes

Researchers say studying individuals, not large groups, is the only way to accurate conclusions

BERKELEY, Calif. — People like to read studies. This, we at StudyFinds, know to be true. But we also know that people like to debate — and often debunk — the veracity and viability of studies, too. Now a study that actually studied studies seems to side with the naysayers, finding that research which evaluates large groups of people leads to skewed results. In order to get a better and more accurate grasp of mankind, the authors say, scientists need to study people individually.

Researchers from the University of California at Berkeley believe that big data can be a big mess, especially for health practitioners who depend on medical research to guide them in their practices. That’s because human beings can be so markedly different from one another, often-studied subjects like mental health and physiology can yield unreliable conclusions when coming from massive segments of a population.

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In order to get a better and more accurate grasp of mankind, scientists need to conduct studies on people individually, not in large groups, a new study finds.

“Diseases, mental disorders, emotions, and behaviors are expressed within individual people, over time. A snapshot of many people at one moment in time can’t capture these phenomena,” argues study lead author Aaron Fisher, an assistant professor of psychology at the university, in a statement.

Fisher collaborated with scientists from Drexel University and the University of Groningen in the Netherlands to analyze data on hundreds of adults — some mentally or physically sound, others suffering from various conditions such as depression, anxiety, or post-traumatic stress disorder. Participants had completed surveys about their mental health and had their heart rates monitored via electrocardiogram.

Researchers used the data to conduct six different experiments. They sought to find out whether the conclusions of each study would successfully apply to participants individually.

One study that focused on how frequently depression sufferers reported feeling worried. Results tallied from the pool of participants showed that depressed people worry a significant amount. But when the analysis was applied individually, the results were all over the map. Some participants worried hardly at all, while others were notably beyond the group average.

Another experiment that centered around the link between fear and avoidance showed a strong correlation when measured as a group. Yet a significant number of participants who experienced fear had no issues with avoiding various activities.

Across all six experiments, the authors could not show that what was concluded for the group applied to most individuals.

“If you want to know what individuals feel or how they become sick, you have to conduct research on individuals, not on groups,” says Fisher, who argues that studies should simply be modified instead of completely ignored. “People shouldn’t necessarily lose faith in medical or social science. Instead, they should see the potential to conduct scientific studies as a part of routine care. This is how we can truly personalize medicine.”

The full study was published June 18, 2018 in the Proceedings of the National Academy of Sciences journal.


  1. The real truth is: all studies are, is just an excuse for researchers to get funded by taxpayers through government grants. It’s a win-win for researchers. They aren’t held accountable, so they profit no matter what-no matter how ridiculous the project is. What a scam.

  2. I’ll bet the big data studies on so-called “globull warming” are mostly fraudulent.

  3. So the conclusion of the study on studies is that the standard deviation is greater than zero?
    Next, they should study whether water is wet.

  4. It seems like the author of this article doesn’t understand the point of these studies. In the case of the cited mental health study, the point is to try to establish a baseline which individuals can be compared to. Outliers do not necessarily somehow render a study incorrect.

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