Introduction: "Garbage In": What Makes Bad Data
Garbage In: What Makes Bad Data
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Oh data, how do I make thee bad? Let me count the ways... Bad Constructs, bad measurement, sample bias, too little variance, too much story... There are so many ways to get this wrong. Knowing this can help us avoid making important decisions based on bad data.
Up Next in Garbage In: What Makes Bad Data
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Bad Constructs
Lots of things that interest us are hard to measure directly, so we use "constructs" to assess them. But sometimes we create constructs that don't mean what we think they do. Picking a bad construct is most fundamental mistake in data analysis. When we work with a bad construct, our data is bad a...
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Bad Measurement
Even when you measure the right thing, your data will be bad if you measure it in the wrong way. "Accuracy" is a good word that describes what we get from good measurement. But when our measures are not accurate, our data will mislead us.
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Sample Bias
If your sample is biased, it doesn't matter very much what you do with it. With selection bias, sample bias, and more, it's a lot easier to get a biased sample than get a representative one.
