Disease testing isn’t perfect. Illustrating how the prevalence of a disease, including COVID-19, can be estimated using an imperfect test and how probability depends on the prevalence, this blog highlights how a single test result should only be one piece of information used in determining an individual’s infection status.
This blog presents — and solves using Z-transforms — a trivial difference equation incorporating the much touted variable R0.
Recently we tweeted an interesting article on big data, from the Financial Times. The author’s key point is that sampling bias and sampling error are possible even with large data sets. As illustration, the author discusses a classic case where the Literary Digest incorrectly predicted that Alf Landon would beat FDR in the 1936 election.…
In Part One of this blog post, I discussed how to state an experiment in the form of probability spaces. Determining the sample space and the event space is necessary to be able to talk intelligently about probability measures, which is the topic of this post. Approach 1: Counting We’ve figured out the sample space…
Cardinal Peak’s big data practice is expanding as we continue adding data scientists to our staff. In a recent discussion regarding a data set we’re analyzing, a probability problem conceptually equivalent to the following arose: In a room filled with N people, what is the probability that none of them have the same birthday? In…