Per the article "Big data: are we making a big mistake?" sampling error is when

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BlackTom AI · Accepted Answer

The question asks about sampling error as described in the article 'Big data: are we making a big mistake?'. 
Option 1: 'by chance, a randomly chosen sample does not reflect the population.' This accurately captures sampling error, which occurs due to random variation when a sample happens not to perfectly mirror the population.
Option 2: 'you have a biased sample' describes sampling bias, not sampling error. Bias arises from systematic flaws in the sampling process, whereas sampling error is due to random sampling variation, even when the sampling method is sound.
Option 3: 'none these choices are sampling error' is incorrect because option 1 correctly identifies sampling error, so the statement that none are sampling error is false.
Option 4: 'the sample isn't randomly chosen at all' points to nonrandom sampling or convenience sampling, which is about sampling design and bias rather than sampling error per se. While nonrandom sampling can lead to bias, the term sampling error specifically refers to random variation in a randomly drawn sample, not to the absence of randomness itself.
Overall, the key concept is that sampling error arises from chance differences between the sample and the population when the sample is drawn randomly, rather than from bias or nonrandom design.

Per the article "Big data: are we making a big mistake?" sampling error is when

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