Should we continue not to trust the Turk? Another reminder of the importance of measurement

By July 17, 2017machinelearning

From 2013: Don’t trust the Turk

From 2017 (link from Kevin Lewis), from Jesse Chandler and Gabriele Paolacci:

The Internet has enabled recruitment of large samples with specific characteristics. However, when researchers rely on participant self-report to determine eligibility, data quality depends on participant honesty. Across four studies on Amazon Mechanical Turk, we show that a substantial number of participants misrepresent theoretically relevant characteristics . . .

For some purposes you can learn a lot from these online samples, but it depends on context. Measurement is important, and it is underrated in statistics.

The trouble is if you’re cruising along treating “p less than .05” as your criterion of success, then quality of measurement barely matters at all! Gather your data, grab your stars, get published, give your Ted talk, and sell your purported expertise to the world. Statistics textbooks have lots about how to analyze your data, a little bit on random sampling and randomized experimentation, and next to nothing on gathering data with high reliability and validity.

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