1. Going through some old data/code I last worked on 8 years ago. One thing I've learned since then is that when combining different datasets or doing other complicated data processing, it pays to be really anal-retentive about missing data or data that doesn't pass sanity checks
2. Say you have a sample size of 5,000 cases, and of those cases, a dozen or so are missing or look as though they're miscoded. You might think, "That's not bad. I'll just throw those cases out and I'll be fine". And you probably will be fine if that's all there is to it.
3. But more often than you might think, the missing/miscoded/"outlier" cases indicate a larger, more systematic problem with your code or with your data. My advice is to do due diligence on those cases BEFORE moving on to next steps, because errors tend to compound.
