Too Good to be True
Mar 11, 2016•35 min
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Episode description
Today on Data Skeptic, Lachlan Gunn joins us to discuss his recent paper Too Good to be True. This paper highlights a somewhat paradoxical / counterintuitive fact about how unanimity is unexpected in cases where perfect measurements cannot be taken. With large enough data, some amount of error is expected.
The "Too Good to be True" paper highlights three interesting examples which we discuss in the podcast. You can also watch a lecture from Lachlan on this topic via youtube here.
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