Bootstrap Confidence Intervals – not as reliable as their reputation!

Bootstrapping can be used to estimate confidence intervals. Start by resampling the original sample - that is, drawing samples the same size as the original sample WITH REPLACEMENT from the sample itself. This simulates taking many samples from a large population that looks exactly like the sample. This will create an approximate sampling distribution. By taking the middle C% of the data, you can estimate a C% confidence interval. Bootstrapping is the best way to estimate confidence intervals for most quantitative statistics, if a tool is available to perform the process.

I’ve been learning a lot about the deep details of statistics lately, motivated partially by interest and partially by a deep desire not to accidentally put something in the CPM Statistics book that is wrong! My current study is about bootstrapping confidence intervals. If you’ve never heard of bootstrapping, I’ll give you the introductory definition weRead More

Exploring the “Large Population” rule – a problem to aid students

In my last post I explored the “Large Population” or “10%” condition for statistical inference using the traditional formulas, specifically as it relates to proportions. After much twitter conversation, I am coming around to the point of view that this should be explored deeply with students, if possible, as it will generate good conversations about precision, samplingRead More

Using simulation to understand a statistical rule of thumb

Inspired by Bob Lochel’s beautiful investigation on when binomial distributions appear normal, I started exploring, for myself, the other rule of thumb in statistical inference: the “Large Population” or “10% rule”. For the uninitiated, a (very) brief explanation. Consider a population of 10 people where 70% of them like chocolate chip cookies – that is, 7 people.Read More