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Data Overload: The Pitfalls of Over-Aggregation in Polling

In this modern, information-driven world, the amount of polling data becomes overwhelming. If putting together information from multiple polls can give a wider perspective, over-aggregation carries great risks. It isn’t all about the numbers in polling; it’s about understanding human behavioral patterns and the context.

John Zogby, independent pollster and founder of the trendspotting research company John Zogby Strategies, said, “One of the largest issues with over-aggregation is that nuance gets lost. Polls are snapshots in time, often reflecting unique conditions at the time they are taken in question wording or current events. When multiple polls are combined, these specific conditions may blur and obscure valuable insights. For example, one poll might get a late-breaking trend that aggregated data misses because it averages out the momentary shift.”

Another trap is the illusory air of certainty that averaging can produce. When polls are averaged, the margin of error is often simply ignored, which gives a misleading impression of precision. An aggregation might show one candidate leading another by 1 or 2 points, but the actual margin of error may be so large that the race is really much closer than it appears.

It can mask subgroup dynamics: key variations in voter behavior across demographic groups often get obscured when the focus is squarely on topline results. This was precisely the case in the 2016 U.S. presidential election, when key trends among swing state voters were missed because of relying too much on the aggregated national polls.

In polling, more data does not always mean better insights. To avoid data overload, aggregation must be balanced with careful analysis of individual polls and the stories they tell.

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