Steven Sanderson pulls us up by the bootstraps:
Imagine this: You have a dataset, say, car mileage (MPG) from the classic
mtcarsdataset. You want to understand the average MPG, but what if that average is just a mirage? What if it’s skewed by a few outliers or doesn’t capture the full story?Enter bootstrapping, a statistical technique that’s like taking your data on a wild ride. It creates multiple copies of your data, each with a slight twist, and then calculates the statistic you’re interested in (e.g., average MPG) for each copy. This gives you a distribution of possible averages, revealing the variability and potential biases lurking beneath the surface.
Read on to learn more about bootstrapping in general and how to use the bootstrap_stat_plot() function in TidyDensity.