This is a simple illustration of Collider bias (also called Berkson's paradox). Because we are relying on a dataset which over-represents some subjects and under-represents others, the ‘paradox’ can lead us to conclude that, for two completely unrelated factors (such as 'looks‘ and 'personality’) one has a causal influence on the other (‘attractive' people are more likely to be 'mean’). This is an edited part of a longer video which shows that we could also wrongly conclude that the true causal relationship between two factors is the opposite of what it is in reality (e.g. ‘skipping lectures results in better grades’). See for full explanation of Berkson's paradox
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