Is Big Data Always Better Data?

IO psychology isn’t immune to popular trends, and the most popular trend to hit our world recently is “big data.” It’s easy to get carried away by the promises of huge samples, but is bigger always better?

In this article from the Free Range Statistics blog, statistician and data scientist Peter Ellis takes us back to basics and demonstrates why a small random sample usually beats a large, non-random sample.

Ellis uses a concrete example to make the point intuitively and provides the R code to show how it works out in practice.

About the Author

Ben Butina, Ph.D.
Dr. Butina, who hosts the Department 12 Podcast, is an industrial-organizational psychologist with interests in training, leadership development, talent management, and positive psychology in the workplace.

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