BurnBright Basics to Understanding Data
Episode 2 • 4m 15s
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 at the most basic level--it simply does not mean what we think it means.
Up Next in Garbage In: What Makes Bad Data
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.
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.
Problems with Variance
If every employee gets a positive evaluation, then what is the purpose of the evaluation in the first place? Without variance in data, we can't really draw any conclusions about what data means. Yet often in the workplace we value consistency so much that we drive variance out of key measures. Th...