{"id":26305,"date":"2021-02-17T09:18:25","date_gmt":"2021-02-17T13:18:25","guid":{"rendered":"https:\/\/www.shortform.com\/blog\/?p=26305"},"modified":"2021-02-24T19:59:22","modified_gmt":"2021-02-24T23:59:22","slug":"aggregation-statistics","status":"publish","type":"post","link":"https:\/\/www.shortform.com\/blog\/aggregation-statistics\/","title":{"rendered":"Aggregation Statistics: The Power of Collective Data"},"content":{"rendered":"\n

What is the function of aggregation statistics? How does aggregating data from multiple sources influence forecast accuracy<\/a>?<\/p>\n\n\n\n

Aggregation statistics combine data from multiple sources. Aggregate numbers are often used in forecasting to obtain a more accurate prediction.<\/p>\n\n\n\n

Read about aggregation statistics as it relates to forecasting.<\/p>\n\n\n\n\n\n\n\n

What Are Aggregation Statistics?<\/h2>\n\n\n\n

In the context of forecasting, aggregation statistics are a powerful tool, because the aggregated collective judgment of a group of people is usually more accurate than the judgment of an average member of the group.<\/strong>\u00a0<\/p>\n\n\n\n

Even if some individual judgments are more accurate than the aggregate number, the collective judgment is still more likely to be accurate than if you were to select a group member at random and use their individual answer. This phenomenon is often called \u201cthe wisdom of crowds,”<\/strong> named for the 2004 book that popularized the idea. <\/p>\n\n\n\n

(A classic example of the wisdom of crowds effect comes from a 1906 country fair, where British scientist Sir Francis Galton observed a crowd of people attempt to guess the weight of an ox. The average of all their guesses was only one pound different from the true answer.) <\/p>\n\n\n\n

The reason this works is that when a group of people faces a single problem, each person has a small piece of the puzzle.<\/strong> In the country fair example, the crowd may have included a butcher, a farmer, and someone who had been part of the same guessing game at last year\u2019s fair. Each person contributed a piece of information\u2014adding those pieces together gave a much more accurate picture of the situation than any individual person in the crowd possessed. <\/p>\n\n\n\n