Dealing with Missing Data in Spirometry Analysis

Imagine you're looking at a bunch of data about how well people can breathe, like in a spirometry test. But guess what? Some of the data is missing! There are empty spots where no numbers exist. Now, you might think we can just ignore those gaps, but we can't. We need all the data to make sense of things.

Sometimes, those missing numbers can actually tell us something cool. Let me explain with an example: think about a table that shows how many newspapers were sold each day. But oh no, there's a gap on March 27th, and it says zero newspapers were sold. Weird, right? This missing number can mess up our calculations, especially if we want to predict future sales.

But why was there a gap on March 27th? Well, there can be many reasons. Maybe someone forgot to write down the sales that day. In this case, it turns out the newsstand was closed because it was a public holiday. Now we know why, but what should we do with that missing zero?

Here are a few options:

1. Delete it: We can just get rid of the incomplete data, but that means losing a chunk of our info, which isn't great.
2. Guess: We can fill in the gap with an estimate. But we have to be smart about it. Putting zero might not make sense because if the store was open, they'd probably sell some papers. So, we could guess using the average sales from all the other days, which is around 37.23 papers. Or, we can look at the pattern (sales going up and down) and guess based on last Sunday's sales, which might be around 40 papers.

Missing data is different from censored data, where we have some info but not all. That's a whole other story.

In the end, dealing with missing data can be tricky, and there's no one-size-fits-all answer. We have to understand why it's missing, use our brains to fill in the gaps and be careful not to mess up our analysis. How we handle those missing numbers can make a big difference in our data adventures.